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Inductive essays: tips, examples, and topics, carla johnson.

June 14, 2023
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Inductive essays are a common type of academic writing. To come to a conclusion, you have to look at the evidence and figure out what it all means. Inductive essays start with a set of observations or evidence and then move toward a conclusion. Deductive essays start with a thesis statement and then give evidence to support it. This type of essay is often used in the social sciences, humanities, and natural sciences.

The goal of an inductive essay is to look at the evidence and draw a conclusion from it. It requires carefully analyzing and interpreting the evidence and being able to draw logical conclusions from it. Instead of starting with a conclusion in mind and trying to prove it, the goal is to use the evidence to build a case for that conclusion.

You can’t say enough about how important it is to look at evidence before coming to a conclusion. In today’s world, where information is easy to find and often contradictory, it is important to be able to sort through the facts to come to a good decision. It is also important to be able to tell when the evidence isn’t complete or doesn’t prove anything, and to be able to admit when there is uncertainty.

In the sections that follow, we’ll talk about some tips for writing good inductive essays, show you some examples of good inductive essays, and give you some ideas for topics for your next inductive essay. By the end of this article, you’ll know more about how to write an inductive essay well.

What You'll Learn

Elements of an Inductive Essay

Most of the time, an inductive essay has three main parts: an intro, body paragraphs, and a conclusion.

The introduction should explain what the topic is about and show the evidence that will be looked at in the essay . It should also have a thesis statement that sums up the conclusion that will be drawn from the evidence.

In the body paragraphs, you should show and explain the evidence. Each paragraph should focus on one piece of evidence and explain how it supports the thesis statement . The analysis should make sense and be well-supported, and there should be a clear link between the evidence and the conclusion.

In the conclusion, you should sum up the evidence and the conclusion you came to based on it. It should also put the conclusion in a bigger picture by explaining why it’s important and what it means for the topic at hand.

How to Choose a Topic for an Inductive Essay

It can be hard to choose a topic for an inductive essay, but there are a few things you can do that will help.

First, it’s important to look at the assignment prompt carefully. What’s the question you’re supposed to answer? What evidence do you have to back up your claim? To choose a topic that is both possible and interesting , you need to understand the prompt and the evidence you have.

Next, brainstorming can be a good way to come up with ideas. Try writing down all the ideas that come to mind when you think about the prompt. At this point, it doesn’t matter if the ideas are good or not. The goal is to come up with as many ideas as possible.

Once you have a list of possible topics , it’s important to pick one that you can handle and that you’re interested in. Think about how big the topic is and if you will have enough time to analyze the evidence in enough depth for the assignment . Also, think about your own passions and interests. If you choose a topic that really interests you, you are more likely to write a good essay .

Some potential topics for an inductive essay include:

– The impact of social media on mental health

– The effectiveness of alternative medicine for treating chronic pain

– The causes of income inequality in the United States

– The relationship between climate change and extreme weather events

– The effects of video game violence on children

By following these tips for choosing a topic and understanding the elements of an inductive essay, you can master the art of this type of academic writing and produce compelling and persuasive essays that draw on evidence to arrive at sound conclusions.

Inductive Essay Outline

An outline can help you to organize your thoughts and ensure that your essay is well-structured. An inductive essay outline typically includes the following sections:

– Introduction: The introduction should provide background information on the topic and present the evidence that will be analyzed in the essay . It should also include a thesis statement that summarizes the conclusion that will be drawn from the evidence.

– Body Paragraphs: The body paragraphs should present the evidence and analyze it in depth. Each paragraph should focus on a specific piece of evidence and explain how it supports the thesis statement . The analysis should be logical and well-supported, with clear connections made between the evidence and the conclusion.

– Conclusion: The conclusion should summarize the evidence and the conclusion that was drawn from it. It should also provide a broader context for the conclusion, explaining why it matters and what implications it has for the topic at hand.

Inductive Essay Structure

The structure of an inductive essay is similar to that of other types of academic essays. It typically includes the following elements:

– Thesis statement: The thesis statement should summarize the conclusion that will be drawn from the evidence and provide a clear focus for the essay .

– Introduction: The introduction should provide background information on the topic and present the evidence that will be analyzed in the essay. It should also include a thesis statement that summarizes the conclusion that will be drawn from the evidence.

– Body Paragraphs: The body paragraphs should present the evidence and analyze it in depth. Each paragraph should focus on a specific piece of evidence and explain how it supports the thesis statement. The analysis should be logical and well-supported, with clear connections made between the evidence and the conclusion.

It is important to note that the body paragraphs can be organized in different ways depending on the nature of the evidence and the argument being made. For example, you may choose to organize the paragraphs by theme or chronologically. Regardless of the organization, each paragraph should be focused and well-supported with evidence.

By following this structure, you can ensure that your inductive essay is well-organized and persuasive, drawing on evidence to arrive at a sound conclusion. Remember to carefully analyze the evidence, and to draw logical connections between the evidence and the conclusion. With practice, you can master the art of inductive essays and become a skilled academic writer.

Inductive Essay Examples

Examples of successful inductive essays can provide a helpful model for your own writing. Here are some examples of inductive essay topics:

– Example 1: The Link Between Smoking and Lung Cancer: This essay could look at the studies and statistics that have been done on the link between smoking and lung cancer and come to a conclusion about how strong it is.

– Example 2: The Effects of Social Media on Mental Health: This essay could look at the studies and personal experiences that have been done on the effects of social media on mental health to come to a conclusion about the effects of social media on mental health.

– Example 3: The Effects of Climate Change on Agriculture: This essay could look at the studies and expert opinions on the effects of climate change on agriculture to come to a conclusion about how it might affect food production..

– Example 4: The Benefits of a Plant-Based Diet: This essay could look at the available evidence about the benefits of a plant-based diet, using studies and dietary guidelines to come to a conclusion about the health benefits of this type of diet.

– Example 5: The Effects of Parenting Styles on Child Development: This essay could look at the studies and personal experiences that have been done on the effects of parenting styles on child development and come to a conclusion about the best way to raise a child.

Tips for Writing an Effective Inductive Essay

Here are some tips for writing acompelling and effective inductive essay:

1. Presenting evidence in a logical and organized way: It is important to present evidence in a clear and organized way that supports the thesis statement and the conclusion. Use topic sentences and transitions to make the connections between the evidence and the conclusion clear for the reader.

2. Considering alternative viewpoints: When analyzing evidence, it is important to consider alternative viewpoints and opinions. Acknowledge counterarguments and address them in your essay, demonstrating why your conclusion is more compelling.

3. Using strong and credible sources: Use credible sources such as peer-reviewed journal articles , statistics, and expert opinions to support your argument. Avoid relying on unreliable sources or anecdotal evidence.

4. Avoiding fallacies and biases: Be aware of logical fallacies and biases that can undermine the credibility of your argument. Avoid making assumptions or jumping to conclusions without sufficient evidence.

By following these tips, you can write an effective inductive essay that draws on evidence to arrive at a sound conclusion. Remember to carefully analyze the evidence, consider alternative viewpoints, and use credible sources to support your argument. With practice and dedication, you can master the art of inductive essays and become a skilled academic writer.

Frequently Asked Questions

1. what is an inductive essay.

An inductive essay is an academic writing that starts with a set of observations or evidence and then works towards a conclusion. The essay requires careful analysis and interpretation of evidence, and the ability to draw logical conclusions based on that evidence.

2. What are the elements of an inductive essay?

An inductive essay typically consists of an introduction, body paragraphs, and a conclusion. The introduction provides background information and presents the thesis statement. The body paragraphs present the evidence and analyze it in depth. The conclusion summarizes the evidence and the conclusion drawn from it.

3. How do I choose a topic for an inductive essay?

To choose a topic for an inductive essay, carefully analyze the assignment prompt, brainstorm ideas, narrow down the topic, and select a topic that interests you.

4. What is the difference between an inductive essay and a deductive essay?

An inductive essay starts with evidence and works towards a conclusion, while a deductive essay starts with a thesis statement and provides arguments to support it.

5. How do I structure an inductive essay?

An inductive essay typically follows a structure that includes a thesis statement, introduction, body paragraphs, and conclusion.

Inductive essays are an important type of academic writing that require careful analysis and interpretation of evidence to come to a conclusion. By using the advice in this article, you can become a good inductive essay writer. Remember to carefully look at the evidence, think about other points of view, use reliable sources, and stay away from logical errors and biases. In conclusion , learning how to write inductive essays is important for developing critical thinking skills and making arguments that are compelling and convincing. You can make a valuable contribution to your field of study and to society as a whole by looking at the facts and coming to logical conclusions. With practice and hard work , you can learn to write good inductive essays that will help you in school and in your career.

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Deductive and Inductive Reasoning Essay

Inductive and deductive reasoning: essay introduction, deductive approach, inductive approach, inductive vs deductive: essay conclusion, reference list.

There are different types of reasoning, most of which are explained in psychology books and articles. This paper discusses two types of reasoning – deductive and inductive reasoning using cognitive research. The inductive and deductive reasoning essay you read focuses on teaching science and technical courses in High Schools. It explores cases of science and mathematical teaching in schools.

Deductive reasoning is a logical process where conclusions are made from general cases. General cases are studied, after which conclusions are made as they apply to a certain case (Byrne, Evans and Newstead, 2019). In the context of this deductive reasoning essay, an argument from analogy is one of the examples under deductive reasoning. The rule underlying this module is that in the case where P and Q are similar and have properties a, b, and c, object P has an extra property, “x.” Therefore, Q will automatically have the same extra property, “x,” as the two are similar (Dew Jr and Foreman, 2020).

Most high school students in the United States do come across the argument from the analogy model of deductive reasoning while studying science subjects. Nonetheless, most students do not realize the applicability of this rule. They apply the rule unconsciously. Therefore, high school students should learn about this model of reasoning. This will help them know certain instances under which they should apply this rule when making arguments in science subjects (National Academies of Sciences, Engineering, and Medicine, 2019).

Researches conducted on analogies give a clear way of explaining why student reports have added ideas. While studying scientific subjects, students do make productive analogies. They apply scientific principles, for instance, energy conservation principles, to different settings.

Unproductive analogies are also made by students, for example, in experiments between temperature and heat. Research that compares different forms of analogies gained from visual and animated representations. Such studies distinguish the functions of different brain parts. It emphasizes the benefits of activating correct pathways for specific learning forms. Research on analogies emphasizes on the selection and inclusion of right analogies in the reports. It also encourages the analysis of different analogies (Vygotsky, 2020).

Argument from analogy is one of the tools that students can use to advance reasonable arguments in different science subjects. This is according to a study that was conducted to ascertain the model that can be used by high school students in when solving problems in genetics. Different questions and student-teacher engagements were used to reach the conclusion (Choden and Kijkuakul, 2020).

The major problems in the teaching of science subjects are the lapses in communication. More often, students and teachers in science classrooms rarely share similar purpose on either the subject or the activity. At times, teachers and students assign different meanings to the same concept. This happens in cases where the two have different levels of understanding about the science concepts because most of these concepts are technical (Choden and Kijkuakul, 2020).

In order to improve the understanding of science subjects, students are required to use different approaches. For students to use analogy, they must have an understanding of the concept in question first. The concept is the most important thing as arguments derived from the subject will be concrete when the concept is well grasped.

More models should be used by science teachers in the science classes. The real nature of the models or analogs used for teaching are better understood when they are realistic. Analogs are forms of human interventions in learning. They should be used carefully as poor use may result in mal understanding of the real meaning. Analogs have an aspect of practicality which leaves images in the minds of students.

When used well, a constructive learning environment will be attained. Analogies should be used in a way that students can easily capture or map. Students should also be given room to make suggestions of improving the analogies used by their teachers. Imperfect analogies expose difficulties that arise in describing and explaining scientific ideas that are mostly of an abstract nature (Newton, 2022).

According to Oaksford and Chater (2020), inductive reasoning entails taking certain examples and using the examples to develop a general principle. It cannot be utilized in proving a concept. In inductive reasoning, solutions to problems can be reached even when the person offering the solution does not have general knowledge about the world.

An example of deductive reasoning is the case of ‘Rex the dog’. In this case, a child can make a deduction that is logical when Rex barks even at times when barking itself is an unfamiliar activity. If the child was told that Rex is a cat and that all cats bark, the child would respond with a “yes” when asked whether Rex barks. This is even when Rex does not bark. Under this reasoning, logical deductions are counterfactual in that they are not made in line with the beliefs of the real world (Pellegrino and Glaser, 2021).

On the other hand, inductive reasoning is one of the oldest learning models. Inductive reasoning develops with time as students grow. However, this reasoning has not been fully utilized in schools. It carries many cognitive skills within it. Inductive thinking is used in creative arts in high schools. In creative art subjects, students are expected to build on their learned ideas. The knowledge learned is applied in different contexts. This is the real goal of inductive reasoning (Csapó, 2020).

For the purposes of the inductive reasoning essay, research has revealed that deductive reasoning can be applied in two performance contexts. This includes the school knowledge application and the applicable knowledge context. School knowledge is the knowledge that is acquired at school. This knowledge is mostly applied in situations that are related to schoolwork.

It is applied in a similar context in which it was acquired. This knowledge or reasoning is what the students apply in handling assignments, tests, and examinations in school. It is used to grade students and determine student careers in schools. Applicable knowledge can be easily applied in situations that differ from the context in which the command was acquired (Csapó, 2020).

Research conducted in the United States revealed that the skills students acquire at the elementary level are insufficient. Elementary mathematics teaching lacks a conceptual explanation to the students. When these students get to high school, they need a basis upon which they can understand mathematical formulas and measurements. Therefore, teachers are forced to introduce these students to a higher level of thinking.

The tasks in high school mathematics that require deep thinking are also called high cognitive demand tasks. At this level of thinking, students can understand complex mathematical concepts and apply them correctly. Thus, students are introduced to inductive reasoning (Brahier, 2020).

Students will mostly have a tough time at the introductory to inductive reasoning. Students will get a grasp of concepts, mostly mathematical ones. However, it will take longer for students to develop application skills. Mathematical concepts will be understood by students within a short span.

However, applying the concepts to solve different mathematical problems is another problem. Just like for the two types of knowledge, it has always been hard for students from high school to apply the school concept in the real world. Students acquire the inside, but in most cases, they reserve it for schoolwork only.

When students do not get good tutoring, gaining the transition required to achieve the real concepts becomes difficult. This idea further destroys them and may even cause a total failure to understand and apply inductive reasoning (Van Vo and Csapó, 2022).

The transition from elementary school to high school includes psychological changes. These changes need to be molded by introducing the student to detailed thinking. This gradual process begins with slowly ushering the students to simple concepts. This simple concept builds slowly, and complexity is introduced gradually.

The students’ minds grow as they get used to the hard concepts. Later, the students become more creative and critical in thinking and understanding concepts (Hayes et al., 2019).

Inductive and deductive reasoning are two types of reasoning that borrow from one another. The use of logical conclusion applies in both of them. They are very useful, especially in teaching mathematics and science courses.

Brahier, D. (2020) Teaching secondary and middle school mathematics . Abingdon: Routledge.

Byrne, R.M., Evans, J.S.B. and Newstead, S.E. (2019) Human reasoning: the psychology of deduction . London: Psychology Press.

Choden, T. and Kijkuakul, S. (2020) ‘Blending problem based learning with scientific argumentation to enhance students’ understanding of basic genetics’, International Journal of Instruction , 13(1), pp. 445-462.

Csapó, B. (2020) ‘Development of inductive reasoning in students across school grade levels’, Thinking Skills and Creativity , 37, pp. 1-15.

Dew Jr, J.K. and Foreman, M.W. (2020) How do we know?: an introduction to epistemology . Westmont: InterVarsity Press.

Hayes, B.K. et al. (2019) ‘The diversity effect in inductive reasoning depends on sampling assumptions’, Psychonomic Bulletin & Review , 26, pp.1043-1050.

National Academies of Sciences, Engineering, and Medicine (2019) Science and engineering for grades 6-12: investigation and design at the center . Washington, D.C.: National Academies Press.

Newton, D.P. (2022) A practical guide to teaching science in the secondary school . Milton Park: Taylor & Francis.

Oaksford, M. and Chater, N. (2020) ‘New paradigms in the psychology of reasoning’, Annual Review of Psychology , 71, pp. 305-330.

Pellegrino, J.W. and Glaser, R. (2021) ‘Components of inductive reasoning’, In Aptitude, learning, and instruction (pp. 177-218). Abingdon: Routledge.

Upmeier zu Belzen, A., Engelschalt, P. and Krüger, D. (2021) ‘Modeling as scientific reasoning – the role of abductive reasoning for modeling competence’, Education Sciences , 11(9), pp. 1-11.

Van Vo, D. and Csapó, B. (2022) ‘Exploring students’ science motivation across grade levels and the role of inductive reasoning in science motivation’, European Journal of Psychology of Education , 37(3), pp. 807-829.

Vygotsky, L.S. (2020) Educational psychology . Boca Raton: CRC Press.

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Inductive Reasoning | Types, Examples, Explanation

Published on 4 May 2022 by Pritha Bhandari . Revised on 5 December 2022.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning , where you go from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

Note: Inductive reasoning is often confused with deductive reasoning. However, in deductive reasoning, you make inferences by going from general premises to specific conclusions.

What is inductive reasoning, inductive reasoning in research, types of inductive reasoning, inductive generalisation, statistical generalisation, causal reasoning, sign reasoning, analogical reasoning, inductive vs deductive reasoning, frequently asked questions about inductive reasoning.

Inductive reasoning is a logical approach to making inferences, or conclusions. People often use inductive reasoning informally in everyday situations.

You may have come across inductive logic examples that come in a set of three statements. These start with one specific observation, add a general pattern, and end with a conclusion.

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In inductive research, you start by making observations or gathering data. Then, you take a broad view of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

You distribute a survey to pet owners. You ask about the type of animal they have and any behavioural changes they’ve noticed in their pets since they started working from home. These data make up your observations.

To analyse your data, you create a procedure to categorise the survey responses so you can pick up on repeated themes. You notice a pattern : most pets became more needy and clingy or agitated and aggressive.

Inductive reasoning is commonly linked to qualitative research , but both quantitative and qualitative research use a mix of different types of reasoning.

There are many different types of inductive reasoning that people use formally or informally, so we’ll cover just a few in this article:

Inductive reasoning generalisations can vary from weak to strong, depending on the number and quality of observations and arguments used.

Inductive generalisations use observations about a sample to come to a conclusion about the population it came from.

Inductive generalisations are also called induction by enumeration.

The flamingos here are all pink.
All flamingos I’ve ever seen are pink.
All flamingos must be pink.

Inductive generalisations are evaluated using several criteria:

Large sample: Your sample should be large for a solid set of observations.
Random sampling : Probability sampling methods let you generalise your findings.
Variety: Your observations should be externally valid .
Counterevidence: Any observations that refute yours falsify your generalisation.

Statistical generalisations use specific numbers to make statements about populations, while non-statistical generalisations aren’t as specific.

These generalisations are a subtype of inductive generalisations, and they’re also called statistical syllogisms.

Here’s an example of a statistical generalisation contrasted with a non-statistical generalisation.

Causal reasoning means making cause-and-effect links between different things.

A causal reasoning statement often follows a standard setup:

You start with a premise about a correlation (two events that co-occur).
You put forward the specific direction of causality or refute any other direction.
You conclude with a causal statement about the relationship between two things.
All of my white clothes turn pink when I put a red cloth in the washing machine with them.
My white clothes don’t turn pink when I wash them on their own.
Putting colourful clothes with light colours causes the colours to run and stain the light-coloured clothes.

Good causal inferences meet a couple of criteria:

Direction: The direction of causality should be clear and unambiguous based on your observations.
Strength: There’s ideally a strong relationship between the cause and the effect.

Sign reasoning involves making correlational connections between different things.

Using inductive reasoning, you infer a purely correlational relationship where nothing causes the other thing to occur. Instead, one event may act as a ‘sign’ that another event will occur or is currently occurring.

Every time Punxsutawney Phil casts a shadow on Groundhog Day, winter lasts six more weeks.
Punxsutawney Phil doesn’t cause winter to be extended six more weeks.
His shadow is a sign that we’ll have six more weeks of wintery weather.

It’s best to be careful when making correlational links between variables . Build your argument on strong evidence, and eliminate any confounding variables , or you may be on shaky ground.

Analogical reasoning means drawing conclusions about something based on its similarities to another thing. You first link two things together and then conclude that some attribute of one thing must also hold true for the other thing.

Analogical reasoning can be literal (closely similar) or figurative (abstract), but you’ll have a much stronger case when you use a literal comparison.

Analogical reasoning is also called comparison reasoning.

Humans and laboratory rats are extremely similar biologically, sharing over 90% of their DNA.
Lab rats show promising results when treated with a new drug for managing Parkinson’s disease.
Therefore, humans will also show promising results when treated with the drug.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

In deductive reasoning, you make inferences by going from general premises to specific conclusions. You start with a theory, and you might develop a hypothesis that you test empirically. You collect data from many observations and use a statistical test to come to a conclusion about your hypothesis.

Inductive research is usually exploratory in nature, because your generalisations help you develop theories. In contrast, deductive research is generally confirmatory.

Sometimes, both inductive and deductive approaches are combined within a single research study.

Inductive reasoning approach

You begin by using qualitative methods to explore the research topic, taking an inductive reasoning approach. You collect observations by interviewing workers on the subject and analyse the data to spot any patterns. Then, you develop a theory to test in a follow-up study.

Deductive reasoning approach

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

There are many different types of inductive reasoning that people use formally or informally.

Here are a few common types:

Inductive generalisation : You use observations about a sample to come to a conclusion about the population it came from.
Statistical generalisation: You use specific numbers about samples to make statements about populations.
Causal reasoning: You make cause-and-effect links between different things.
Sign reasoning: You make a conclusion about a correlational relationship between different things.
Analogical reasoning: You make a conclusion about something based on its similarities to something else.

In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

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Understanding Inductive Reasoning and Analogy
Philosophical thinking

In inductive reasoning, the observations are used to draw a general conclusion, rather than reaching a specific conclusion from a single observation. In other words, the conclusion is based on an accumulation of data, rather than a single observation. For example, if one observes several instances of birds with wings, one could then conclude that all birds have wings. An example of inductive reasoning in a philosophical context would be the following: if one has observed that all human beings have certain traits in common, one can then draw the general conclusion that all human beings share some common traits.

An example of an analogy in a philosophical context would be to compare the human soul to a spark of divine light. Analogical reasoning can be used to form arguments in philosophical thinking. By comparing two or more objects, one can draw a conclusion about their similarities and differences. This can then be used to support or refute an argument or hypothesis.

For example, one might compare two different theories about the nature of the universe and conclude that one theory is more valid than the other. Inductive reasoning and analogy are both important tools for forming arguments in philosophical thinking. Inductive reasoning allows one to draw general conclusions from multiple observations, while analogy allows one to compare two or more objects and draw inferences from the comparison. Both approaches have strengths and weaknesses.

Strengths and Weaknesses of Each Approach

What is inductive reasoning.

These inferences can then be used to form more general conclusions. This type of reasoning is based on the idea that if something has been true in the past, it is likely to be true in the future. This type of thinking is often used to come up with scientific theories and laws, as well as philosophical arguments. In order to use inductive reasoning effectively, it is important to have a good understanding of the facts and evidence that are being used to form the conclusion. It is also important to be aware of any potential biases or logical fallacies that may be present in the conclusion.

How Can Inductive Reasoning and Analogy be Used to Form Arguments?

Inductive reasoning is a form of argumentation in which a conclusion is drawn from a set of observations or facts. The conclusion that is drawn is not necessarily certain, but rather is likely or probable. Analogy is also a form of argumentation in which two objects or situations are compared and the similarities between them are used to draw conclusions about one or both of them. Both forms of argumentation can be used to form arguments, though each has its own strengths and weaknesses. When using inductive reasoning, the strength of the argument is based on the number of observations or facts that support it.

The more observations or facts that support the argument, the stronger it is. However, the presence of even one observation or fact that does not support the argument can weaken it significantly. It is also important to remember that an inductive argument can never provide certainty, as there is always the possibility that future observations or facts could contradict the conclusions reached. Analogical arguments rely on similarities between two objects or situations to draw conclusions about one or both of them. Analogies are generally considered strong arguments because they provide a comparison between two things that may seem unrelated, allowing for a deeper understanding of each one.

What is Analogy?

For example, let's say you are trying to explain to someone why it's important to exercise regularly. You could use the analogy of a car engine, saying that just as a car engine needs regular maintenance and tune-ups to run properly, our bodies also need regular exercise and nutrition to stay healthy. Analogies can also be used to make more abstract points, such as in politics or philosophy. For example, you might use the analogy of a river to explain the concept of a free market economy - just as a river naturally finds its own level, so too does the free market find its own equilibrium.

Analogies can be powerful tools for understanding complex ideas, as they provide an easy way for people to visualize and understand concepts that may otherwise be too abstract or difficult to grasp. However, it is important to remember that analogies are only useful up to a point - they should not be taken too literally or used to make overly simplistic assumptions about a situation. In conclusion, inductive reasoning and analogy are two important tools for philosophical thinking. They can both be used to form arguments, although each has its own strengths and weaknesses.

Understanding both approaches can help philosophers make effective arguments and make sound judgments. Inductive reasoning allows us to draw conclusions from specific observations and experiences, while analogy helps us to draw comparisons between disparate ideas or objects. Each approach has its own advantages and disadvantages, so it is important to understand when it is best to use each one. With a thorough understanding of inductive reasoning and analogy, philosophers can make sound judgments and effective arguments.

Exploring Plato's Republic

Understanding Existentialism: A Brief Introduction

Exploring Friedrich Nietzsche: An Introduction to the Major Modern Philosopher

Exploring Pragmatism: A Modern Philosophical School
Exploring the Life and Work of Georg Wilhelm Friedrich Hegel
German Idealism: A Comprehensive Overview
Explore The Epicurean School of Ancient Philosophy
Analytic Philosophy: A Primer
Exploring the Philosophy of Immanuel Kant
Exploring British Empiricism
Justification: A Comprehensive Overview
Exploring the History and Impact of Empiricism
Enlightenment: A History of Philosophy
Understanding Fallacies and Rebuttals
Exploring Applied Ethics
Exploring Critical Thinking and Decision Making
Exploring Theology: A Comprehensive Overview
Exploring the Rationalism of Renaissance Philosophy
Exploring Moral Psychology: A Closer Look
Exploring Identity Theory and Personal Identity
Exploring Quantitative Research Methods in Philosophy
Exploring the Analytic-Synthetic Distinction in Philosophy of Language
Exploring the Role of Research Design and Methodology
Understanding the Meaning of Words and Phrases
The Problem of Evil and Suffering: A Philosophical Exploration
Exploring the Interplay between Religious Experience and Faith
Exploring the Concepts of Cause, Necessity, Possibility, and Impossibility
Comparing Analogy, Syllogism, Deduction and Induction
Exploring Aesthetic Theories: Formalism, Emotionalism and Experientialism

Understanding Inference, Validity, Soundness, and Refutation

Exploring Relational Aesthetics, Sociological Aesthetics, and Historical Aesthetics
Exploring Naturalistic, Immanent and Transcendental Aesthetics
Exploring Rationalism and Empiricism
Understanding Determinism, Fatalism, and Libertarianism
Exploring Identity, Persistence, Time, and Space
Understanding Virtue Ethics, Utilitarianism and Deontology
Exploring Phenomenalism and Nominalism
Ethical Theories: Virtue Ethics, Utilitarianism, and Deontology
Exploring Justification, Beliefs and Truths
Foundationalism and Coherentism: An Overview
Exploring Social Contract Theory, Natural Law Theory, and Care Ethics
Exploring Subjectivism, Egoism and Hedonism
The Art of Rhetoric by Cicero: A Comprehensive Overview
The Critique of Pure Reason by Immanuel Kant: A Comprehensive Overview

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Inductive Logic

An inductive logic is a logic of evidential support. In a deductive logic, the premises of a valid deductive argument logically entail the conclusion, where logical entailment means that every logically possible state of affairs that makes the premises true must make the conclusion true as well. Thus, the premises of a valid deductive argument provide total support for the conclusion. An inductive logic extends this idea to weaker arguments. In a good inductive argument, the truth of the premises provides some degree of support for the truth of the conclusion, where this degree-of-support might be measured via some numerical scale. By analogy with the notion of deductive entailment, the notion of inductive degree-of-support might mean something like this: among the logically possible states of affairs that make the premises true, the conclusion must be true in (at least) proportion r of them—where r is some numerical measure of the support strength.

If a logic of good inductive arguments is to be of any real value, the measure of support it articulates should be up to the task. Presumably, the logic should at least satisfy the following condition:

Criterion of Adequacy (CoA) : The logic should make it likely (as a matter of logic) that as evidence accumulates, the total body of true evidence claims will eventually come to indicate, via the logic’s measure of support , that false hypotheses are probably false and that true hypotheses are probably true.

The CoA stated here may strike some readers as surprisingly strong. Given a specific logic of evidential support, how might it be shown to satisfy such a condition? Section 4 will show precisely how this condition is satisfied by the logic of evidential support articulated in Sections 1 through 3 of this article.

This article will focus on the kind of the approach to inductive logic most widely studied by epistemologists and logicians in recent years. This approach employs conditional probability functions to represent measures of the degree to which evidence statements support hypotheses. Presumably, hypotheses should be empirically evaluated based on what they say (or imply) about the likelihood that evidence claims will be true. A straightforward theorem of probability theory, called Bayes’ Theorem, articulates the way in which what hypotheses say about the likelihoods of evidence claims influences the degree to which hypotheses are supported by those evidence claims. Thus, this approach to the logic of evidential support is often called a Bayesian Inductive Logic or a Bayesian Confirmation Theory . This article will first provide a detailed explication of a Bayesian approach to inductive logic. It will then examine the extent to which this logic may pass muster as an adequate logic of evidential support for hypotheses. In particular, we will see how such a logic may be shown to satisfy the Criterion of Adequacy stated above.

Sections 1 through 3 present all of the main ideas underlying the (Bayesian) probabilistic logic of evidential support. These three sections should suffice to provide an adequate understanding of the subject. Section 5 extends this account to cases where the implications of hypotheses about evidence claims (called likelihoods ) are vague or imprecise. After reading Sections 1 through 3, the reader may safely skip directly to Section 5, bypassing the rather technical account in Section 4 of how how the CoA is satisfied.

Section 4 is for the more advanced reader who wants an understanding of how this logic may bring about convergence to the true hypothesis as evidence accumulates. This result shows that the Criterion of Adequacy is indeed satisfied—that as evidence accumulates, false hypotheses will very probably come to have evidential support values (as measured by their posterior probabilities ) that approach 0; and as this happens, a true hypothesis may very probably acquire evidential support values (as measured by its posterior probability ) that approaches 1.

1. Inductive Arguments

2.1 the historical origins of probabilistic logic, 2.2 probabilistic logic: axioms and characteristics, 2.3 two conceptions of inductive probability, 3.1 likelihoods, 3.2 posterior probabilities and prior probabilities, 3.3 bayes’ theorem, 3.4 on prior probabilities and representations of vague and diverse plausibility assessments, 4.1 the space of possible outcomes of experiments and observations, 4.2 probabilistic independence, 4.3 likelihood ratio convergence when falsifying outcomes are possible, 4.4 likelihood ratio convergence when no falsifying outcomes are possible, 5. when the likelihoods are vague or diverse, list of supplements, other internet resources, related entries.

Let us begin by considering some common kinds of examples of inductive arguments. Consider the following two arguments:

Example 1. Every raven in a random sample of 3200 ravens is black. This strongly supports the following conclusion: All ravens are black.

Example 2. 62 percent of voters in a random sample of 400 registered voters (polled on February 20, 2004) said that they favor John Kerry over George W. Bush for President in the 2004 Presidential election. This supports with a probability of at least .95 the following conclusion: Between 57 percent and 67 percent of all registered voters favor Kerry over Bush for President (at or around the time the poll was taken).

This kind of argument is often called an induction by enumeration . It is closely related to the technique of statistical estimation. We may represent the logical form of such arguments semi-formally as follows:

Premise: In random sample S consisting of n members of population B , the proportion of members that have attribute A is r .

Therefore, with degree of support p ,

Conclusion: The proportion of all members of B that have attribute A is between $r-q$ and $r+q$ (i.e., lies within margin of error q of r ).

Let’s lay out this argument more formally. The premise breaks down into three separate statements: [ 1 ]

Any inductive logic that treats such arguments should address two challenges. (1) It should tell us which enumerative inductive arguments should count as good inductive arguments. In particular, it should tell us how to determine the appropriate degree p to which such premises inductively support the conclusion, for a given margin of error q . (2) It should demonstrably satisfy the CoA . That is, it should be provable (as a metatheorem) that if a conclusion expressing the approximate proportion for an attribute in a population is true, then it is very likely that sufficiently numerous random samples of the population will provide true premises for good inductive arguments that confer degrees of support p approaching 1 for that true conclusion—where, on pain of triviality, these sufficiently numerous samples are only a tiny fraction of a large population. The supplement on Enumerative Inductions: Bayesian Estimation and Convergence , shows precisely how a a Bayesian account of enumerative induction may meet these two challenges.

Enumerative induction is, however, rather limited in scope. This form of induction is only applicable to the support of claims involving simple universal conditionals (i.e., claims of form ‘All B s are A s’) and claims about the proportion of an attribute in a population (i.e., claims of form ‘the frequency of A s among the B s is r ’). But, many important empirical hypotheses are not reducible to this simple form, and the evidence for these hypotheses is not composed of an enumeration of such instances. Consider, for example, the Newtonian Theory of Mechanics:

All objects remain at rest or in uniform motion unless acted upon by some external force. An object’s acceleration (i.e., the rate at which its motion changes from rest or from uniform motion) is in the same direction as the force exerted on it; and the rate at which the object accelerates due to a force is equal to the magnitude of the force divided by the object’s mass. If an object exerts a force on another object, the second object exerts an equal amount of force on the first object, but in the opposite direction to the force exerted by the first object.

The evidence for (and against) this theory is not gotten by examining a randomly selected subset of objects and the forces acting upon them. Rather, the theory is tested by calculating what this theory says (or implies) about observable phenomena in a wide variety of specific situations—e.g., ranging from simple collisions between small bodies to the trajectories of planets and comets—and then seeing whether those phenomena occur in the way that the theory says they will. This approach to testing hypotheses and theories is ubiquitous, and should be captured by an adequate inductive logic.

More generally, for a wide range of cases where inductive reasoning is important, enumerative induction is inadequate. Rather, the kind of evidential reasoning that judges the likely truth of hypotheses on the basis of what they say (or imply) about the evidence is more appropriate. Consider the kinds of inferences jury members are supposed to make, based on the evidence presented at a murder trial. The inference to probable guilt or innocence is based on a patchwork of evidence of various kinds. It almost never involves consideration of a randomly selected sequences of past situations when people like the accused committed similar murders. Or, consider how a doctor diagnoses her patient on the basis of his symptoms. Although the frequency of occurrence of various diseases when similar symptoms have been present may play a role, this is clearly not the whole story. Diagnosticians commonly employ a form of hypothesis evaluation —e.g., would the hypothesis that the patient has a brain tumor account for his symptoms?; or are these symptoms more likely the result of a minor stroke?; or may some other hypothesis better account for the patient’s symptoms? Thus, a fully adequate account of inductive logic should explicate the logic of hypothesis evaluation , through which a hypothesis or theory may be tested on the basis of what it says (or "predicts") about observable phenomena. In Section 3 we will see how a kind of probabilistic inductive logic called "Bayesian Inference" or "Bayesian Confirmation Theory" captures such reasoning. The full logical structure of such arguments will be spelled out in that section.

2. Inductive Logic and Inductive Probabilities

Perhaps the oldest and best understood way of representing partial belief, uncertain inference, and inductive support is in terms of probability and the equivalent notion odds . Mathematicians have studied probability for over 350 years, but the concept is certainly much older. In recent times a number of other, related representations of partial belief and uncertain inference have emerged. Some of these approaches have found useful application in computer based artificial intelligence systems that perform inductive inferences in expert domains such as medical diagnosis. Nevertheless, probabilistic representations have predominated in such application domains. So, in this article we will focus exclusively on probabilistic representations of inductive support. A brief comparative description of some of the most prominent alternative representations of uncertainty and support-strength can be found in the supplement Some Prominent Approaches to the Representation of Uncertain Inference .

The mathematical study of probability originated with Blaise Pascal and Pierre de Fermat in the mid-17 th century. From that time through the early 19 th century, as the mathematical theory continued to develop, probability theory was primarily applied to the assessment of risk in games of chance and to drawing simple statistical inferences about characteristics of large populations—e.g., to compute appropriate life insurance premiums based on mortality rates. In the early 19 th century Pierre de Laplace made further theoretical advances and showed how to apply probabilistic reasoning to a much wider range of scientific and practical problems. Since that time probability has become an indispensable tool in the sciences, business, and many other areas of modern life.

Throughout the development of probability theory various researchers appear to have thought of it as a kind of logic. But the first extended treatment of probability as an explicit part of logic was George Boole’s The Laws of Thought (1854). John Venn followed two decades later with an alternative empirical frequentist account of probability in The Logic of Chance (1876). Not long after that the whole discipline of logic was transformed by new developments in deductive logic.

In the late 19 th and early 20 th century Frege, followed by Russell and Whitehead, showed how deductive logic may be represented in the kind of rigorous formal system we now call quantified predicate logic . For the first time logicians had a fully formal deductive logic powerful enough to represent all valid deductive arguments that arise in mathematics and the sciences. In this logic the validity of deductive arguments depends only on the logical structure of the sentences involved. This development in deductive logic spurred some logicians to attempt to apply a similar approach to inductive reasoning. The idea was to extend the deductive entailment relation to a notion of probabilistic entailment for cases where premises provide less than conclusive support for conclusions. These partial entailments are expressed in terms of conditional probabilities , probabilities of the form $P[C \pmid B] = r$ (read “the probability of C given B is r ”), where P is a probability function, C is a conclusion sentence, B is a conjunction of premise sentences, and r is the probabilistic degree of support that premises B provide for conclusion C . Attempts to develop such a logic vary somewhat with regard to the ways in which they attempt to emulate the paradigm of formal deductive logic.

Some inductive logicians have tried to follow the deductive paradigm by attempting to specify inductive support probabilities solely in terms of the syntactic structures of premise and conclusion sentences. In deductive logic the syntactic structure of the sentences involved completely determines whether premises logically entail a conclusion. So these inductive logicians have attempted to follow suit. In such a system each sentence confers a syntactically specified degree of support on each of the other sentences of the language. Thus, the inductive probabilities in such a system are logical in the sense that they depend on syntactic structure alone. This kind of conception was articulated to some extent by John Maynard Keynes in his Treatise on Probability (1921). Rudolf Carnap pursued this idea with greater rigor in his Logical Foundations of Probability (1950) and in several subsequent works (e.g., Carnap 1952). (For details of Carnap’s approach see the section on logical probability in the entry on interpretations of the probability calculus , in this Encyclopedia .)

In the inductive logics of Keynes and Carnap, Bayes’ theorem, a straightforward theorem of probability theory, plays a central role in expressing how evidence comes to bear on hypotheses. Bayes’ theorem expresses how the probability of a hypothesis h on the evidence e , $P[h \pmid e]$, depends on the probability that e should occur if h is true, $P[e \pmid h]$, and on the probability of hypothesis h prior to taking the evidence into account, $P[h]$ (called the prior probability of h ). (Later we’ll examine Bayes’ theorem in detail.) So, such approaches might well be called Bayesian logicist inductive logics. Other prominent Bayesian logicist attempts to develop a probabilistic inductive logic include the works of Jeffreys (1939), Jaynes (1968), and Rosenkrantz (1981).

It is now widely held that the core idea of this syntactic approach to Bayesian logicism is fatally flawed—that syntactic logical structure cannot be the sole determiner of the degree to which premises inductively support conclusions. A crucial facet of the problem faced by syntactic Bayesian logicism involves how the logic is supposed to apply in scientific contexts where the conclusion sentence is some scientific hypothesis or theory, and the premises are evidence claims. The difficulty is that in any probabilistic logic that satisfies the usual axioms for probabilities, the inductive support for a hypothesis must depend in part on its prior probability . This prior probability represents (arguably) how plausible the hypothesis is taken to be on the basis of considerations other than the observational and experimental evidence (e.g., perhaps due to various plausibility arguments). A syntactic Bayesian logicist must tell us how to assign values to these pre-evidential prior probabilities of hypotheses in a way that relies only on the syntactic logical structure of the hypothesis, perhaps based on some measure of syntactic simplicity. There are severe problems with getting this idea to work. Various kinds of examples seem to show that such an approach must assign intuitively quite unreasonable prior probabilities to hypotheses in specific cases (see the footnote cited near the end of Section 3.2 for details). Furthermore, for this idea to apply to the evidential support of real scientific theories, scientists would have to formalize theories in a way that makes their relevant syntactic structures apparent, and then evaluate theories solely on that syntactic basis (together with their syntactic relationships to evidence statements). Are we to evaluate alternative theories of gravitation, and alternative quantum theories, this way? This seems an extremely dubious approach to the evaluation of real scientific hypotheses and theories. Thus, it seems that logical structure alone may not suffice for the inductive evaluation of scientific hypotheses. (This issue will be treated in more detail in Section 3 , after we first see how probabilistic logics employ Bayes’ theorem to represent the evidential support for hypotheses as a function of prior probabilities together with evidential likelihoods .)

At about the time that the syntactic Bayesian logicist idea was developing, an alternative conception of probabilistic inductive reasoning was also emerging. This approach is now generally referred to as the Bayesian subjectivist or personalist approach to inductive reasoning (see, e.g., Ramsey 1926; De Finetti 1937; Savage 1954; Edwards, Lindman, & Savage 1963; Jeffrey 1983, 1992; Howson & Urbach 1993; Joyce 1999). This approach treats inductive probability as a measure of an agent’s degree-of-belief that a hypothesis is true, given the truth of the evidence. This approach was originally developed as part of a larger normative theory of belief and action known as Bayesian decision theory . The principal idea is that the strength of an agent’s desires for various possible outcomes should combine with her belief-strengths regarding claims about the world to produce optimally rational decisions. Bayesian subjectivists provide a logic of decision that captures this idea, and they attempt to justify this logic by showing that in principle it leads to optimal decisions about which of various risky alternatives should be pursued. On the Bayesian subjectivist or personalist account of inductive probability, inductive probability functions represent the subjective (or personal) belief-strengths of ideally rational agents, the kind of belief strengths that figure into rational decision making. (See the section on subjective probability in the entry on interpretations of the probability calculus , in this Encyclopedia .)

Elements of a logicist conception of inductive logic live on today as part of the general approach called Bayesian inductive logic . However, among philosophers and statisticians the term ‘Bayesian’ is now most closely associated with the subjectivist or personalist account of belief and decision. And the term ‘Bayesian inductive logic’ has come to carry the connotation of a logic that involves purely subjective probabilities. This usage is misleading since, for inductive logics, the Bayesian/non-Bayesian distinction should really turn on whether the logic gives Bayes’ theorem a prominent role, or the approach largely eschews the use of Bayes’ theorem in inductive inferences, as do the classical approaches to statistical inference developed by R. A. Fisher (1922) and by Neyman & Pearson (1967)). Indeed, any inductive logic that employs the same probability functions to represent both the probabilities of evidence claims due to hypotheses and the probabilities of hypotheses due to those evidence claims must be a Bayesian inductive logic in this broader sense; because Bayes’ theorem follows directly from the axioms that each probability function must satisfy, and Bayes’ theorem expresses a necessary connection between the probabilities of evidence claims due to hypotheses and the probabilities of hypotheses due to those evidence claims .

In this article the probabilistic inductive logic we will examine is a Bayesian inductive logic in this broader sense. This logic will not presuppose the subjectivist Bayesian theory of belief and decision, and will avoid the objectionable features of the syntactic version of Bayesian logicism. We will see that there are good reasons to distinguish inductive probabilities from degree-of-belief probabilities and from purely syntactic logical probabilities . So, the probabilistic logic articulated in this article will be presented in a way that depends on neither of these conceptions of what the probability functions are . However, this version of the logic will be general enough that it may be fitted to a Bayesian subjectivist or Bayesian syntactic-logicist program, if one desires to do that.

All logics derive from the meanings of terms in sentences. What we now recognize as formal deductive logic rests on the meanings (i.e., the truth-functional properties) of the standard logical terms. These logical terms, and the symbols we will employ to represent them, are as follows:

‘not’, ‘${\nsim}$’;
‘and’, ‘$\cdot$’;
‘inclusive or’, ‘$\vee$’;
truth-functional ‘if-then’, ‘$\supset$’;
‘if and only if’, ‘$\equiv$’;
‘all’, ‘$\forall$’, and
‘some’, ‘$\exists$’;
the identity relation, ‘=’.

The meanings of all other terms, the non-logical terms such as names and predicate and relational expressions, are permitted to “float free”. That is, the logical validity of deductive arguments depends neither on the meanings of the name and predicate and relation terms, nor on the truth-values of sentences containing them. It merely supposes that these non-logical terms are meaningful, and that sentences containing them have truth-values. Deductive logic then tells us that the logical structures of some sentences—i.e., the syntactic arrangements of their logical terms—preclude them from being jointly true of any possible state of affairs. This is the notion of logical inconsistency . The notion of logical entailment is inter-definable with it. A collection of premise sentences logically entails a conclusion sentence just when the negation of the conclusion is logically inconsistent with those premises.

An inductive logic must, it seems, deviate from the paradigm provided by deductive logic in several significant ways. For one thing, logical entailment is an absolute, all-or-nothing relationship between sentences, whereas inductive support comes in degrees-of-strength. For another, although the notion of inductive support is analogous to the deductive notion of logical entailment , and is arguably an extension of it, there seems to be no inductive logic extension of the notion of logical inconsistency —at least none that is inter-definable with inductive support in the way that logical inconsistency is inter-definable with logical entailment . Indeed, it turns out that when the unconditional probability of $(B\cdot{\nsim}A)$ is very nearly 0 (i.e., when $(B\cdot{\nsim}A)$ is “nearly inconsistent”), the degree to which B inductively supports A , $P[A \pmid B]$, may range anywhere between 0 and 1.

Another notable difference is that when B logically entails A , adding a premise C cannot undermine the logical entailment—i.e., $(C\cdot B)$ must logically entail A as well. This property of logical entailment is called monotonicity . But inductive support is nonmonotonic . In general, depending on what $A, B$, and C mean, adding a premise C to B may substantially raise the degree of support for A , or may substantially lower it, or may leave it completely unchanged—i.e., $P[A \pmid (C\cdot B)]$ may have a value much larger than $P[A \pmid B]$, or may have a much smaller value, or it may have the same, or nearly the same value as $P[A \pmid B]$.

In a formal treatment of probabilistic inductive logic, inductive support is represented by conditional probability functions defined on sentences of a formal language L . These conditional probability functions are constrained by certain rules or axioms that are sensitive to the meanings of the logical terms (i.e., ‘not’, ‘and’, ‘or’, etc., the quantifiers ‘all’ and ‘some’, and the identity relation). The axioms apply without regard for what the other terms of the language may mean. In essence the axioms specify a family of possible support functions , $\{P_{\beta}, P_{\gamma}, \ldots ,P_{\delta}, \ldots \}$ for a given language L . Although each support function satisfies these same axioms, the further issue of which among them provides an appropriate measure of inductive support is not settled by the axioms alone. That may depend on additional factors, such as the meanings of the non-logical terms (i.e., the names and predicate expressions) of the language.

A good way to specify the axioms of the logic of inductive support functions is as follows. These axioms are apparently weaker than the usual axioms for conditional probabilities. For instance, the usual axioms assume that conditional probability values are restricted to real numbers between 0 and 1. The following axioms do not assume this, but only that support functions assign some real numbers as values for support strengths. However, it turns out that the following axioms suffice to derive all the usual axioms for conditional probabilities (including the usual restriction to values between 0 and 1). We draw on these weaker axioms only to forestall some concerns about whether the support function axioms may assume too much, or may be overly restrictive.

Let L be a language for predicate logic with identity, and let ‘$\vDash$’ be the standard logical entailment relation—i.e., the expression ‘$B \vDash A$’ says “ B logically entails A ” and the expression ‘$\vDash A$’ says “ A is a tautology”. A support function is a function $P_{\alpha}$ from pairs of sentences of L to real numbers that satisfies the following axioms:

(1) $P_{\alpha}[E \pmid F] \ne P_{\alpha}[G \pmid H]$ for at least some sentences $E, F, G$, and H .

For all sentence $A, B, C$, and D :

(2) If $B \vDash A$, then $P_{\alpha}[A \pmid B] \ge P_{\alpha}[C \pmid D]$;
(3) $P_{\alpha}[A \pmid (B \cdot C)] = P_{\alpha}[A \pmid (C \cdot B)]$;
(4) If $C \vDash{\nsim}(B \cdot A)$, then either \[P_{\alpha}[(A \vee B) \pmid C] = P_{\alpha}[A \pmid C] + P_{\alpha}[B \pmid C]\] or else \[P_{\alpha}[E \pmid C] = P_{\alpha}[C \pmid C]\] for every sentence E ;
(5) $P_{\alpha}[(A \cdot B) \pmid C] = P_{\alpha}[A \pmid (B \cdot C)] \times P_{\alpha}[B \pmid C]$.

This axiomatization takes conditional probability as basic, as seems appropriate for evidential support functions . (These functions agree with the more usual unconditional probability functions when the latter are defined—just let $P_{\alpha}[A] = P_{\alpha}[A \pmid (D \vee{\nsim}D)]$. However, these axioms permit conditional probabilities $P_{\alpha}[A \pmid C]$ to remain defined even when condition statement C has probability 0—i.e., even when $P_{\alpha}[C \pmid (D\vee{\nsim}D)] = 0$.)

Notice that conditional probability functions apply only to pairs of sentences, a conclusion sentence and a premise sentence. So, in probabilistic inductive logic we represent finite collections of premises by conjoining them into a single sentence. Rather than say,

A is supported to degree r by the set of premises $\{B_1$, $B_2$, $B_3$,…, $B_n\}$,

we instead say that

A is supported to degree r by the conjunctive premise $(((B_1\cdot B_2)\cdot B_3)\cdot \ldots \cdot B_n)$,

and write this as

The above axioms are quite weak. For instance, they do not say that logically equivalent sentences are supported by all other sentences to the same degree; rather, that result is derivable from these axioms (see result 6 below). Nor do these axioms say that logically equivalent sentences support all other sentences to the same degree; rather, that result is also derivable (see result 8 below). Indeed, from these axioms all of the usual theorems of probability theory may be derived. The following results are particularly useful in probabilistic logic. Their derivations from these axioms are provided in note 2. [ 2 ]

If $B \vDash A$, then $P_{\alpha}[A \pmid B] = 1$.
If $C \vDash{\nsim}(B\cdot A)$, then either \[P_{\alpha}[(A \vee B) \pmid C] = P_{\alpha}[A \pmid C] + P_{\alpha}[B \pmid C]\] or else $P_{\alpha}[E \pmid C] = 1$ for every sentence E .
$P_{\alpha}[{\nsim}A \pmid B] = 1 - P_{\alpha}[A \pmid B]$ or else $P_{\alpha}[C \pmid B] = 1$ for every sentence C .
$1 \ge P_{\alpha}[A \pmid B] \ge 0$.
If $B \vDash A$, then $P_{\alpha}[A \pmid C] \ge P_{\alpha}[B \pmid C]$.
If $B \vDash A$ and $A \vDash B$, then $P_{\alpha}[A \pmid C] = P_{\alpha}[B \pmid C]$.
If $C \vDash B$, then $P_{\alpha}[(A\cdot B) \pmid C] = P_{\alpha}[(B\cdot A) \pmid C] = P_{\alpha}[A \pmid C]$.
If $C \vDash B$ and $B \vDash C$, then $P_{\alpha}[A \pmid B] = P_{\alpha}[A \pmid C]$.
$P_{\alpha}[B \pmid C] \gt 0$, then \[P_{\alpha}[A \pmid (B\cdot C)] = P_{\alpha}[B \pmid (A\cdot C)] \times \frac{P_{\alpha}[A \pmid C]}{P_{\alpha}[B \pmid C]}\] (this is a simple form of Bayes’ theorem).
$P_{\alpha}[(A\vee B) \pmid C] = P_{\alpha}[A \pmid C] + P_{\alpha}[B \pmid C] - P_{\alpha}[(A\cdot B) \pmid C]$.
If $\{B_1 , \ldots ,B_n\}$ is any finite set of sentences such that for each pair $B_i$ and $B_j, C \vDash{\nsim}(B_{i}\cdot B_{j})$ (i.e., the members of the set are mutually exclusive, given C ), then either $P_{\alpha}[D \pmid C] = 1$ for every sentence D , or \[ P_{\alpha}[((B_1\vee B_2)\vee \ldots \vee B_n) \pmid C] = \sum ^{n}_{i=1} P_{\alpha}[B_i \pmid C]. \]
If $\{B_1 , \ldots ,B_n , \ldots \}$ is any countably infinite set of sentences such that for each pair $B_i$ and $B_j, C \vDash{\nsim}(B_{i}\cdot B_{j})$, then either $P_{\alpha}[D \pmid C] = 1$ for every sentence D , or [ 3 ] \[ \lim_n P_{\alpha}[((B_1\vee B_2)\vee \ldots \vee B_n) \pmid C] = \sum^{\infty}_{i=1} P_{\alpha}[B_i \pmid C]. \]

Let us now briefly consider each axiom to see how plausible it is as a constraint on a quantitative measure of inductive support, and how it extends the notion of deductive entailment. First notice that each degree-of-support function $P_{\alpha}$ on L measures support strength with some real number values, but the axioms don’t explicitly restrict these values to lie between 0 and 1. It turns out that the all support values must lie between 0 and 1, but this follows from the axioms, rather than being assumed by them. The scaling of inductive support via the real numbers is surely a reasonable way to go.

Axiom 1 is a non-triviality requirement. It says that the support values cannot be the same for all sentence pairs. This axiom merely rules out the trivial support function that assigns the same amount of support to each sentence by every sentence. One might replace this axiom with the following rule:

But this alternative rule turns out to be derivable from axiom 1 together with the other axioms.

Axiom 2 asserts that when B logically entail A , the support of A by B is as strong as support can possibly be. This comports with the idea that an inductive support function is a generalization of the deductive entailment relation, where the premises of deductive entailments provide the strongest possible support for their conclusions.

Axiom 3 merely says that $(B \cdot C)$ supports sentences to precisely the same degree that $(C \cdot B)$ supports them. This is an especially weak axiom. But taken together with the other axioms, it suffices to entail that logically equivalent sentences support all sentences to precisely the same degree.

Axiom 4 says that inductive support adds up in a plausible way. When C logically entails the incompatibility of A and B , i.e., when no possible state of affairs can make both A and B true together, the degrees of support that C provides to each of them individually must sum to the support it provides to their disjunction. The only exception is in those cases where C acts like a logical contradiction and supports all sentences to the maximum possible degree (in deductive logic a logical contradiction logically entails every sentence).

To understand what axiom 5 says, think of a support function $P_{\alpha}$ as describing a measure on possible states of affairs. Read each degree-of-support expression of form ‘$P_{\alpha}[D \pmid E] = r$’ to say that the proportion of states of affairs in which D is true among those states of affairs where E is true is r . Read this way, axiom 5 then says the following. Suppose B is true in proportion q of all the states of affairs where C is true, and suppose A is true in fraction r of those states where B and C are true together. Then A and B should be true together in what proportion of all the states where C is true? In fraction r (the $(A\cdot B)$ part) of proportion q (the B portion) of all those states where C is true.

The degree to which a sentence B supports a sentence A may well depend on what these sentences mean. In particular it will usually depend on the meanings we associate with the non-logical terms (those terms other than the logical terms not , and , or , etc., the quantifiers , and identity ), that is, on the meanings of the names, and the predicate and relation terms of the language. For example, we should want

given the usual meanings of ‘bachelor’ and ‘married’, since “all bachelors are unmarried” is analytically true—i.e. no empirical evidence is required to establish this connection. (In the formal language for predicate logic, if we associate the meaning “is married” with predicate term ‘ M ’, the meaning “is a bachelor” with the predicate term ‘ B ’, and take the name term ‘ g ’ to refer to George, then we should want $P_{\alpha}[{\nsim}Mg \pmid Bg] = 1$, since $\forall x (Bx \supset{\nsim}Mx)$ is analytically true on this meaning assignment to the non-logical terms.) So, let’s associate with each individual support function $P_{\alpha}$ a specific assignment of meanings ( primary intensions ) to all the non-logical terms of the language. (However, evidential support functions should not presuppose meaning assignments in the sense of so-called secondary intensions —e.g., those associated with rigid designators across possible states of affairs. For, we should not want a confirmation function $P_{\alpha}$ to make

since we presumably want the inductive logic to draw on explicit empirical evidence to support the claim that water is made of H 2 O. Thus, the meanings of terms we associate with a support function should only be their primary intensions, not their secondary intensions.)

In the context of inductive logic it makes good sense to supplement the above axioms with two additional axioms. Here is the first of them:

(6) If A is an axiom of set theory or any other piece of pure mathematics employed by the sciences, or if A is analytically true (i.e., if the truth of A depends only on the meanings of the words it contains, where the specific meanings for names and predicates are those associated with the particular support function $P_{\alpha})$, then, for all sentences C , $P_{\alpha}[A \pmid C] = P_{\alpha}[C \pmid C]$ (i.e., $P_{\alpha}[A \pmid C] = 1)$.

Here is how axiom 6 applies to the above example, yielding $P_{\alpha}[{\nsim}Mg \pmid Bg] = 1$ when the meaning assignments to non-logical terms associated with support function $P_{\alpha}$ makes $\forall x(Bx \supset{\nsim}Mx)$ analytically true. From axiom 6 (followed by results 7, 5, and 4) we have

thus, $P_{\alpha}[{\nsim}Mg \pmid Bg] = 1$. The idea behind axiom 6 is that inductive logic is about evidential support for contingent claims. Nothing can count as empirical evidence for or against non-contingent truths. In particular, analytic truths should be maximally supported by all premises C .

One important respect in which inductive logic should follow the deductive paradigm is that the logic should not presuppose the truth of contingent statements. If a statement C is contingent, then some other statements should be able to count as evidence against C . Otherwise, a support function $P_{\alpha}$ will take C and all of its logical consequences to be supported to degree 1 by all possible evidence claims. This is no way for an inductive logic to behave. The whole idea of inductive logic is to provide a measure of the extent to which premise statements indicate the likely truth-values of contingent conclusion statements. This idea won’t work properly if the truth-values of some contingent statements are presupposed by assigning them support value 1 on every possible premise. Such probability assignments would make the inductive logic enthymematic by hiding significant premises in inductive support relationships. It would be analogous to permitting deductive arguments to count as valid in cases where the explicitly stated premises are insufficient to logically entail the conclusion, but where the validity of the argument is permitted to depend on additional unstated premises. This is not how a rigorous approach to deductive logic should work, and it should not be a common practice in a rigorous approach to inductive logic.

Nevertheless, it is common practice for probabilistic logicians to sweep provisionally accepted contingent claims under the rug by assigning them probability 1 (regardless of the fact that no explicit evidence for them is provided). This practice saves the trouble of repeatedly writing a given contingent sentence B as a premise, since $P_{\gamma}[A \pmid B\cdot C]$ will equal $P_{\gamma}[A \pmid C]$ whenever $P_{\gamma}[B \pmid C] = 1$. Although this convention is useful, such probability functions should be considered mere abbreviations for proper, logically explicit, non-enthymematic, inductive support relations. Thus, properly speaking, an inductive support function $P_{\alpha}$ should not assign probability 1 to a sentence on every possible premise unless that sentence is either (i) logically true, or (ii) an axiom of set theory or some other piece of pure mathematics employed by the sciences, or (iii) unless according to the interpretation of the language that $P_{\alpha}$ presupposes, the sentence is analytic (and so outside the realm of evidential support). Thus, we adopt the following version of the so-called “axiom of regularity”.

(7) If, for all C , $P_{\alpha}[A \pmid C] = P_{\alpha}[C \pmid C]$ (i.e., $P_{\alpha}[A \pmid C] = 1$), then A must be a logical truth or an axiom of set theory or some other piece of pure mathematics employed by the sciences, or A must be analytically true (according to the meanings of the terms of L associated with support function $P_{\alpha})$.

Axioms 6 and 7 taken together say that a support function $P_{\alpha}$ counts as non-contingently true, and so not subject to empirical support, just those sentences that are assigned probability 1 by every premise.

Some Bayesian logicists have proposed that an inductive logic might be made to depend solely on the logical form of sentences, as is the case for deductive logic. The idea is, effectively, to supplement axioms 1–7 with additional axioms that depend only on the logical structures of sentences, and to introduce enough such axioms to reduce the number of possible support functions to a single uniquely best support function. It is now widely agreed that this project cannot be carried out in a plausible way. Perhaps support functions should obey some rules in addition to axioms 1–7. But it is doubtful that any plausible collection of additional rules can suffice to determine a single, uniquely qualified support function. Later, in Section 3 , we will briefly return to this issue, after we develop a more detailed account of how inductive probabilities capture the relationship between hypotheses and evidence.

Axioms 1–7 for conditional probability functions merely place formal constraints on what may properly count as a degree of support function . Each function $P_{\alpha}$ that satisfies these axioms may be viewed as a possible way of applying the notion of inductive support to a language L that respects the meanings of the logical terms, much as each possible truth-value assignment for a language represents a possible way of assigning truth-values to its sentences in a way that respects the meanings of the logical terms. The issue of which of the possible truth-value assignments to a language represents the actual truth or falsehood of its sentences depends on more than this. It depends on the meanings of the non-logical terms and on the state of the actual world. Similarly, the degree to which some sentences actually support others in a fully meaningful language must rely on something more than the mere satisfaction of the axioms for support functions. It must, at least, rely on what the sentences of the language mean, and perhaps on much more besides. But, what more? Perhaps a better understanding of what inductive probability is may provide some help by filling out our conception of what inductive support is about. Let’s pause to discuss two prominent views—two interpretations of the notion of inductive probability.

One kind of non-syntactic logicist reading of inductive probability takes each support function $P_{\alpha}$ to be a measure on possible states of affairs. The idea is that, given a fully meaningful language (associated with support function $P_{\alpha}$) ‘$P_{\alpha}[A \pmid B] = r$’ says that among those states of affairs in which B is true, A is true in proportion r of them. There will not generally be a single privileged way to define such a measure on possible states of affairs. Rather, each of a number of functions $P_{\alpha}$, $P_{\beta}$, $P_{\gamma}$,…, etc., that satisfy the constraints imposed by axioms 1–7 may represent a viable measure of the inferential import of the propositions expressed by sentences of the language. This idea needs more fleshing out, of course. The next section will provide some indication of how that might go.

Subjectivist Bayesians offer an alternative reading of the support functions. First, they usually take unconditional probability as basic, and take conditional probabilities as defined in terms of unconditional probabilities: the conditional probability ‘$P_{\alpha}[A \pmid B]$’ is defined as a ratio of unconditional probabilities:

Subjectivist Bayesians take each unconditional probability function $P_{\alpha}$ to represent the belief-strengths or confidence-strengths of an ideally rational agent, $\alpha$. On this understanding ‘$P_{\alpha}[A] =r$’ says, “the strength of $\alpha$’s belief (or confidence) that A is truth is r ”. Subjectivist Bayesians usually tie such belief strengths to how much money (or how many units of utility ) the agent would be willing to bet on A turning out to be true. Roughly, the idea is this. Suppose that an ideally rational agent $\alpha$ would be willing to accept a wager that would yield (no less than) $ u if A turns out to be true and would lose him $1 if A turns out to be false. Then, under reasonable assumptions about the agent’s desire money, it can be shown that the agent’s belief strength that A is true should be

And it can further be shown that any function $P_{\alpha}$ that expresses such betting-related belief-strengths on all statements in agent $\alpha$’s language must satisfy axioms for unconditional probabilities analogous to axioms 1–5. [ 4 ] Moreover, it can be shown that any function $P_{\beta}$ that satisfies these axioms is a possible rational belief function for some ideally rational agent $\beta$. These relationships between belief-strengths and the desirability of outcomes (e.g., gaining money or goods on bets) are at the core of subjectivist Bayesian decision theory . Subjectivist Bayesians usually take inductive probability to just be this notion of probabilistic belief-strength .

Undoubtedly real agents do believe some claims more strongly than others. And, arguably, the belief strengths of real agents can be measured on a probabilistic scale between 0 and 1, at least approximately. And clearly the inductive support of a hypothesis by evidence should influence the strength of an agent’s belief in the truth of that hypothesis—that’s the point of engaging in inductive reasoning, isn’t it? However, there is good reason for caution about viewing inductive support functions as Bayesian belief-strength functions, as we’ll see a bit later. So, perhaps an agent’s support function is not simply identical to his belief function, and perhaps the relationship between inductive support and belief-strength is somewhat more complicated.

In any case, some account of what support functions are supposed to represent is clearly needed. The belief function account and the logicist account (in terms of measures on possible states of affairs) are two attempts to provide this account. But let us put this interpretative issue aside for now. One may be able to get a better handle on what inductive support functions really are after one sees how the inductive logic that draws on them is supposed to work.

3. The Application of Inductive Probabilities to the Evaluation of Scientific Hypotheses

One of the most important applications of an inductive logic is its treatment of the evidential evaluation of scientific hypotheses. The logic should capture the structure of evidential support for all sorts of scientific hypotheses, ranging from simple diagnostic claims (e.g., “the patient is infected by the HIV”) to complex scientific theories about the fundamental nature of the world, such as quantum mechanics or the theory of relativity. This section will show how evidential support functions (a.k.a. Bayesian confirmation functions) represent the evidential evaluation of scientific hypotheses and theories. This logic is essentially comparative. The evaluation of a hypothesis depends on how strongly evidence supports it over alternative hypotheses.

Consider some collection of mutually incompatible, alternative hypotheses (or theories) about a common subject matter, $\{h_1, h_2 , \ldots \}$. The collection of alternatives may be very simple, e.g., {“the patient has HIV”, “the patient is free of HIV”}. Or, when the physician is trying to determine which among a range of diseases is causing the patient’s symptoms, the collection of alternatives may consist of a long list of possible disease hypotheses. For the cosmologist, the collection of alternatives may consist of several distinct gravitational theories, or several empirically distinct variants of the “same” theory. Whenever two variants of a hypothesis (or theory) differ in empirical import, they count as distinct hypotheses. (This should not be confused with the converse positivistic assertion that theories with the same empirical content are really the same theory. Inductive logic doesn’t necessarily endorse that view.)

The collection of competing hypotheses (or theories) to be evaluated by the logic may be finite in number, or may be countably infinite. No realistic language contains more than a countable number of expressions; so it suffices for a logic to apply to countably infinite number of sentences. From a purely logical perspective the collection of competing alternatives may consist of every rival hypothesis (or theory) about a given subject matter that can be expressed within a given language — e.g., all possible theories of the origin and evolution of the universe expressible in English and contemporary mathematics. In practice, alternative hypotheses (or theories) will often be constructed and evidentially evaluated over a long period of time. The logic of evidential support works in much the same way regardless of whether all alternative hypotheses are considered together, or only a few alternative hypotheses are available at a time.

Evidence for scientific hypotheses consists of the results of specific experiments or observations. For a given experiment or observation, let ‘$c$’ represent a description of the relevant conditions under which it is performed, and let ‘$e$’ represent a description of the result of the experiment or observation, the evidential outcome of conditions $c$.

The logical connection between scientific hypotheses and the evidence often requires the mediation of background information and auxiliary hypotheses. Let ‘$b$’ represent whatever background and auxiliary hypotheses are required to connect each hypothesis $h_i$ among the competing hypotheses $\{h_1, h_2 , \ldots \}$ to the evidence. Although the claims expressed by the auxiliary hypotheses within $b$ may themselves be subject to empirical evaluation, they should be the kinds of claims that are not at issue in the evaluation of the alternative hypothesis in the collection $\{h_1, h_2 , \ldots \}$. Rather, each of the alternative hypotheses under consideration draws on the same background and auxiliaries to logically connect to the evidential events. (If competing hypotheses $h_i$ and $h_j$ draw on distinct auxiliary hypotheses $a_i$ and $a_j$, respectively, in making logical contact with evidential claims, then the following treatment should be applied to the respective conjunctive hypotheses, $(h_{i}\cdot a_{i})$ and $(h_{j}\cdot a_{j})$, since these alternative conjunctive hypotheses will constitute the empirically distinct alternatives at issue.)

In cases where a hypothesis is deductively related to an outcome $e$ of an observational or experimental condition $c$ (via background and auxiliaries $b$), we will have either $h_i\cdot b\cdot c \vDash e$ or $h_i\cdot b\cdot c \vDash{\nsim}e$ . For example, $h_i$ might be the Newtonian Theory of Gravitation. A test of the theory might involve a condition statement $c$ that describes the results of some earlier measurements of Jupiter’s position, and that describes the means by which the next position measurement will be made; the outcome description $e$ states the result of this additional position measurement; and the background information (and auxiliary hypotheses) $b$ might state some already well confirmed theory about the workings and accuracy of the devices used to make the position measurements. Then, from $h_i\cdot b\cdot c$ we may calculate the specific outcome $e$ we expect to find; thus, the following logical entailment holds: $h_i\cdot b\cdot c \vDash e$ . Then, provided that the experimental and observational conditions stated by $c$ are in fact true, if the evidential outcome described by $e$ actually occurs, the resulting conjoint evidential claim $(c\cdot e)$ may be considered good evidence for $h_i$, given $b$. (This method of theory evaluation is called the hypothetical-deductive approach to evidential support.) On the other hand, when from $h_i\cdot b\cdot c$ we calculate some outcome incompatible with the observed evidential outcome $e$, then the following logical entailment holds: $h_i\cdot b\cdot c \vDash{\nsim}e$. In that case, from deductive logic alone we must also have that $b\cdot c\cdot e \vDash{\nsim}h_i$ ; thus, $h_i$ is said to be falsified by $b\cdot c\cdot e$. The Bayesian account of evidential support we will be describing below extends this deductivist approach to include cases where the hypothesis $h_i$ (and its alternatives) may not be deductive related to the evidence, but may instead imply that the evidential outcome is likely or unlikely to some specific degree r . That is, the Bayesian approach applies to cases where we may have neither $h_i\cdot b\cdot c \vDash e$ nor $h_i\cdot b\cdot c \vDash{\nsim}e$, but may instead only have $P[e \pmid h_i\cdot b\cdot c] = r$, where r is some “entailment strength” between 0 and 1.

Before going on to describing the logic of evidential support in more detail, perhaps a few more words are in order about the background knowledge and auxiliary hypotheses, represented here by ‘$b$’. Duhem (1906) and Quine (1953) are generally credited with alerting inductive logicians to the importance of auxiliary hypotheses in connecting scientific hypotheses and theories to empirical evidence. (See the entry on Pierre Duhem .) They point out that scientific hypotheses often make little contact with evidence claims on their own. Rather, in most cases scientific hypotheses make testable predictions only relative to background information and auxiliary hypotheses that tie them to the evidence. (Some specific examples of such auxiliary hypotheses will be provided in the next subsection.) Typically auxiliaries are highly confirmed hypotheses from other scientific domains. They often describe the operating characteristics of various devices (e.g., measuring instruments) used to make observations or conduct experiments. Their credibility is usually not at issue in the testing of hypothesis $h_i$ against its competitors, because $h_i$ and its alternatives usually rely on the same auxiliary hypotheses to tie them to the evidence. But even when an auxiliary hypothesis is already well-confirmed, we cannot simply assume that it is unproblematic, or just known to be true . Rather, the evidential support or refutation of a hypothesis $h_i$ is relative to whatever auxiliaries and background information (in $b$) is being supposed in the confirmational context. In other contexts the auxiliary hypotheses used to test $h_i$ may themselves be among a collection of alternative hypotheses that are subject to evidential support or refutation. Furthermore, to the extent that competing hypotheses employ different auxiliary hypotheses in accounting for evidence, the evidence only tests each such hypothesis in conjunction with its distinct auxiliaries against alternative hypotheses packaged with their distinct auxiliaries, as described earlier. Thus, what counts as a hypothesis to be tested , $h_i$, and what counts as auxiliary hypotheses and background information, $b$, may depend on the epistemic context—on what class of alternative hypotheses are being tested by a collection of experiments or observations, and on what claims are presupposed in that context. No statement is intrinsically a test hypothesis , or intrinsically an auxiliary hypothesis or background condition . Rather, these categories are roles statements may play in a particular epistemic context.

In a probabilistic inductive logic the degree to which the evidence $(c\cdot e)$ supports a hypothesis $h_i$ relative to background and auxiliaries $b$ is represented by the posterior probability of $h_i$, $P_{\alpha}[h_i \pmid b\cdot c\cdot e]$, according to an evidential support function $P_{\alpha}$. It turns out that the posterior probability of a hypothesis depends on just two kinds of factors: (1) its prior probability , $P_{\alpha}[h_i \pmid b]$, together with the prior probabilities of its competitors, $P_{\alpha}[h_j \pmid b]$, $P_{\alpha}[h_k \pmid b]$, etc.; and (2) the likelihood of evidential outcomes $e$ according to $h_i$ in conjunction with with $b$ and $c$, $P[e \pmid h_i\cdot b\cdot c]$, together with the likelihoods of these same evidential outcomes according to competing hypotheses, $P[e \pmid h_j\cdot b\cdot c]$, $P[e \pmid h_k\cdot b\cdot c]$, etc. We will now examine each of these factors in some detail. Following that we will see precisely how the values of posterior probabilities depend on the values of likelihoods and prior probabilities.

In probabilistic inductive logic the likelihoods carry the empirical import of hypotheses. A likelihood is a support function probability of form $P[e \pmid h_i\cdot b\cdot c]$. It expresses how likely it is that outcome $e$ will occur according to hypothesis $h_i$ together with the background and auxiliaries $b$ and the experimental (or observational) conditions $c$. [ 5 ] If a hypothesis together with auxiliaries and experimental/observation conditions deductively entails an evidence claim, the axioms of probability make the corresponding likelihood objective in the sense that every support function must agree on its values: $P[e \pmid h_i\cdot b\cdot c] = 1$ if $h_i\cdot b\cdot c \vDash e$; $P[e \pmid h_i\cdot b\cdot c] = 0$ if $h_i\cdot b\cdot c \vDash{\nsim}e$. However, in many cases a hypothesis $h_i$ will not be deductively related to the evidence, but will only imply it probabilistically. There are several ways this might happen: (1) hypothesis $h_i$ may itself be an explicitly probabilistic or statistical hypothesis; (2) an auxiliary statistical hypothesis, as part of the background b , may connect hypothesis $h_i$ to the evidence; (3) the connection between the hypothesis and the evidence may be somewhat loose or imprecise, not mediated by explicit statistical claims, but nevertheless objective enough for the purposes of evidential evaluation. Let’s briefly consider examples of the first two kinds. We’ll treat case (3) in Section 5 , which addresses the issue of vague and imprecise likelihoods.

The hypotheses being tested may themselves be statistical in nature. One of the simplest examples of statistical hypotheses and their role in likelihoods are hypotheses about the chance characteristic of coin-tossing. Let $h_{[r]}$ be a hypothesis that says a specific coin has a propensity (or objective chance ) r for coming up heads on normal tosses, let $b$ say that such tosses are probabilistically independent of one another. Let $c$ state that the coin is tossed n times in the normal way; and let $e$ say that on these tosses the coin comes up heads m times. In cases like this the value of the likelihood of the outcome $e$ on hypothesis $h_{[r]}$ for condition $c$ is given by the well-known binomial formula:

There are, of course, more complex cases of likelihoods involving statistical hypotheses. Consider, for example, the hypothesis that plutonium 233 nuclei have a half-life of 20 minutes—i.e., that the propensity (or objective chance ) for a Pu-233 nucleus to decay within a 20 minute period is 1/2. The full statistical model for the lifetime of such a system says that the propensity (or objective chance ) for that system to remain intact (i.e., to not decay) within any time period x is governed by the formula $1/2^{x/\tau}$, where $\tau$ is the half-life of such a system. Let $h$ be a hypothesis that says that this statistical model applies to Pu-233 nuclei with $\tau = 20$ minutes; let $c$ say that some specific Pu-233 nucleus is intact within a decay detector (of some specific kind) at an initial time $t_0$; let $e$ say that no decay of this same Pu-233 nucleus is detected by the later time $t$; and let $b$ say that the detector is completely accurate (it always registers a real decay, and it never registers false-positive detections). Then, the associated likelihood of $e$ given $h$ and $c$ is this: $P[e \pmid h\cdot b\cdot c] = 1/2^{(t - t_0)/\tau}$, where the value of $\tau$ is 20 minutes.

An auxiliary statistical hypothesis, as part of the background $b$, may be required to connect hypothesis $h_i$ to the evidence. For example, a blood test for HIV has a known false-positive rate and a known true-positive rate. Suppose the false-positive rate is .05—i.e., the test tends to incorrectly show the blood sample to be positive for HIV in 5% of all cases where HIV is not present . And suppose that the true-positive rate is .99—i.e., the test tends to correctly show the blood sample to be positive for HIV in 99% of all cases where HIV really is present . When a particular patient’s blood is tested, the hypotheses under consideration are this patient is infected with HIV , $h$, and this patient is not infected with HIV , ${\nsim}h$. In this context the known test characteristics function as background information, b . The experimental condition $c$ merely states that this particular patient was subjected to this specific kind of blood test for HIV, which was processed by the lab using proper procedures. Let us suppose that the outcome $e$ states that the result is a positive test result for HIV. The relevant likelihoods then, are $P[e \pmid h\cdot b\cdot c] = .99$ and $P[e \pmid {\nsim}h\cdot b\cdot c]$ = .05.

In this example the values of the likelihoods are entirely due to the statistical characteristics of the accuracy of the test, which is carried by the background/auxiliary information $b$. The hypothesis $h$ being tested by the evidence is not itself statistical.

This kind of situation may, of course, arise for much more complex hypotheses. The alternative hypotheses of interest may be deterministic physical theories, say Newtonian Gravitation Theory and some specific alternatives. Some of the experiments that test this theory relay on somewhat imprecise measurements that have known statistical error characteristics, which are expressed as part of the background or auxiliary hypotheses, $b$. For example, the auxiliary $b$ may describe the error characteristics of a device that measures the torque imparted to a quartz fiber, where the measured torque is used to assess the strength of the gravitational force between test masses. In that case $b$ may say that for this kind of device the measurement errors are normally distributed about whatever value a given gravitational theory predicts, with some specified standard deviation that is characteristic of the device. This results in specific values $r_i$ for the likelihoods, $P[e \pmid h_i\cdot b\cdot c] = r_i$, for each of the various gravitational theories, $h_i$, being tested.

Likelihoods that arise from explicit statistical claims—either within the hypotheses being tested, or from explicit statistical background claims that tie the hypotheses to the evidence—are often called direct inference likelihoods . Such likelihoods should be completely objective. So, all evidential support functions should agree on their values, just as all support functions agree on likelihoods when evidence is logically entailed. Direct inference likelihoods are logical in an extended, non-deductive sense. Indeed, some logicians have attempted to spell out the logic of direct inferences in terms of the logical form of the sentences involved. [ 6 ] But regardless of whether that project succeeds, it seems reasonable to take likelihoods of this sort to have highly objective or intersubjectively agreed values.

Not all likelihoods of interest in confirmational contexts are warranted deductively or by explicitly stated statistical claims. In such cases the likelihoods may have vague, imprecise values, but values that are determinate enough to still underwrite an objective evaluation of hypotheses on the evidence. In Section 5 we’ll consider such cases, where no underlying statistical theory is involved, but where likelihoods are determinate enough to play their standard role in the evidential evaluation of scientific hypotheses. However, the proper treatment of such cases will be more easily understood after we have first seen how the logic works when likelihoods are precisely known (such as cases where the likelihood values are endorsed by explicit statistical hypotheses and/or explicit statistical auxiliaries). In any case, the likelihoods that relate hypotheses to evidence claims in many scientific contexts will have such objective values. So, although a variety of different support functions $P_{\alpha}$, $P_{\beta}$,…, $P_{\gamma}$, etc., may be needed to represent the differing “inductive proclivities” of the various members of a scientific community, for now we will consider cases where all evidential support functions agree on the values of the likelihoods. For, the likelihoods represent the empirical content of a scientific hypothesis, what the hypothesis (together with experimental conditions, $c$, and background and auxiliaries $b$) says or probabilistically implies about the evidence. Thus, the empirical objectivity of a science relies on a high degree of objectivity or intersubjective agreement among scientists on the numerical values of likelihoods.

To see the point more vividly, imagine what a science would be like if scientists disagreed widely about the values of likelihoods. Each practitioner interprets a theory to say quite different things about how likely it is that various possible evidence statements will turn out to be true. Whereas scientist $\alpha$ takes theory $h_1$ to probabilistically imply that event $e$ is highly likely, his colleague $\beta$ understands the empirical import of $h_1$ to say that $e$ is very unlikely. And, conversely, $\alpha$ takes competing theory $h_2$ to probabilistically imply that $e$ is very unlikely, whereas $\beta$ reads $h_2$ to say that $e$ is extremely likely. So, for $\alpha$ the evidential outcome $e$ supplies strong support for $h_1$ over $h_2$, because

But his colleague $\beta$ takes outcome $e$ to show just the opposite, that $h_2$ is strongly supported over $h_1$, because

If this kind of situation were to occur often, or for significant evidence claims in a scientific domain, it would make a shambles of the empirical objectivity of that science. It would completely undermine the empirical testability of such hypotheses and theories within that scientific domain. Under these circumstances, although each scientist employs the same sentences to express a given theory $h_i$, each understands the empirical import of these sentences so differently that $h_i$ as understood by $\alpha$ is an empirically different theory than $h_i$ as understood by $\beta$. (Indeed, arguably, $\alpha$ must take at least one of the two sentences, $h_1$ or $h_2$, to express a different proposition than does $\beta$.) Thus, the empirical objectivity of the sciences requires that experts should be in close agreement about the values of the likelihoods. [ 7 ]

For now we will suppose that the likelihoods have objective or intersubjectively agreed values, common to all agents in a scientific community. We mark this agreement by dropping the subscript ‘$\alpha$’, ‘$\beta$’, etc., from expressions that represent likelihoods, since all support functions under consideration are supposed to agree on the values for likelihoods. One might worry that this supposition is overly strong. There are legitimate scientific contexts where, although scientists should have enough of a common understanding of the empirical import of hypotheses to assign quite similar values to likelihoods, precise agreement on their numerical values may be unrealistic. This point is right in some important kinds of cases. So later, in Section 5, we will see how to relax the supposition that precise likelihood values are available, and see how the logic works in such cases. But for now the main ideas underlying probabilistic inductive logic will be more easily explained if we focus on those contexts were objective or intersubjectively agreed likelihoods are available. Later we will see that much the same logic continues to apply in contexts where the values of likelihoods may be somewhat vague, or where members of the scientific community disagree to some extent about their values.

An adequate treatment of the likelihoods calls for the introduction of one additional notational device. Scientific hypotheses are generally tested by a sequence of experiments or observations conducted over a period of time. To explicitly represent the accumulation of evidence, let the series of sentences $c_1$, $c_2$, …, $c_n$, describe the conditions under which a sequence of experiments or observations are conducted. And let the corresponding outcomes of these observations be represented by sentences $e_1$, $e_2$, …, $e_n$. We will abbreviate the conjunction of the first n descriptions of experimental or observational conditions by ‘$c^n$’, and abbreviate the conjunction of descriptions of their outcomes by ‘$e^n$’. Then, for a stream of n observations or experiments and their outcomes, the likelihoods take form $P[e^n \pmid h_{i}\cdot b\cdot c^{n}] = r$, for appropriate values of $r$. In many cases the likelihood of the evidence stream will be equal to the product of the likelihoods of the individual outcomes:

When this equality holds, the individual bits of evidence are said to be probabilistically independent on the hypothesis (together with auxiliaries) . In the following account of the logic of evidential support, such probabilistic independence will not be assumed, except in those places where it is explicitly invoked.

The probabilistic logic of evidential support represents the net support of a hypothesis by the posterior probability of the hypothesis , $P_{\alpha}[h_i \pmid b\cdot c^{n}\cdot e^{n}]$. The posterior probability represents the net support for the hypothesis that results from the evidence, $c^n \cdot e^n$, together with whatever plausibility considerations are taken to be relevant to the assessment of $h_i$. Whereas the likelihoods are the means through which evidence contributes to the posterior probability of a hypothesis, all other relevant plausibility consideration are represented by a separate factor, called the prior probability of the hypothesis : $P_{\alpha}[h_i \pmid b]$. The prior probability represents the weight of any important considerations not captured by the evidential likelihoods. Any relevant considerations that go beyond the evidence itself may be explicitly stated within expression $b$ (in addition to whatever auxiliary hypotheses $b$ may contain in support of the likelihoods). Thus, the prior probability of $h_i$ may depend explicitly on the content of $b$. It turns out that posterior probabilities depend only on the values of evidential likelihoods together with the values of prior probabilities.

As an illustration of the role of prior probabilities , consider the HIV test example described in the previous section. What the physician and the patient want to know is the value of the posterior probability, $P_{\alpha}[h \pmid b\cdot c\cdot e]$, that the patient has HIV, $h$, given the evidence of the positive test, $c\cdot e$, and given the error rates of the test, described within $b$. The value of this posterior probability depends on the likelihood (due to the error rates) of this patient obtaining a true-positive result, $P[e \pmid h\cdot b\cdot c] = .99$, and of obtaining a false-positive result, $P[e \pmid {\nsim}h\cdot b\cdot c] = .05$. In addition, the value of the of the posterior probability depends on how plausible it is that the patient has HIV prior to taking the test results into account, $P_{\alpha}[h \pmid b]$. In the context of medical diagnosis, this prior probability is usually assessed on the basis of the base rate for HIV in the patient’s risk group (i.e., whether the patient is an IV drug user, has unprotected sex with multiple partners, etc.). On a rigorous approach to the logic, such information and its risk-relevance should be explicitly stated within the background information $b$. To see the importance of this information, consider the following numerical results (which may be calculated using the formula called Bayes’ Theorem, presented in the next section). If the base rate for the patient’s risk group is relatively high, say $P_{\alpha}[h \pmid b] = .10$, then the positive test result yields a posterior probability value for his having HIV of $P_{\alpha}[h \pmid b\cdot c\cdot e] = .69$. However, if the patient is in a very low risk group, say $P_{\alpha}[h \pmid b] = .001$, then a positive test result only raises the posterior probability of his having an HIV infection to $P_{\alpha}[h \pmid b\cdot c\cdot e] = .02$. This posterior probability is much higher than the prior probability of .001, but should not worry the patient too much. This positive test result may well be due to the comparatively high false-positive rate for the test, rather than to the presence of HIV. This sort of test, with a false-positive rate as large as .05, is best used as a screening test; a positive result warrants conducting a second, more rigorous, less error-prone test.

More generally, in the evidential evaluation of scientific hypotheses and theories, prior probabilities represent assessments of non-evidential plausibility weightings among hypotheses. However, because the strengths of such plausibility assessments may vary among members of a scientific community, critics often brand such assessments as merely subjective , and take their role in Bayesian inference to be highly problematic. Bayesian inductivists counter that plausibility assessments play an important, legitimate role in the sciences, especially when evidence cannot suffice to distinguish among some alternative hypotheses. And, they argue, the epithet “merely subjective” is unwarranted. Such plausibility assessments are often backed by extensive arguments that may draw on forceful conceptual considerations.

Scientists often bring plausibility arguments to bear in assessing competing views. Although such arguments are seldom decisive, they may bring the scientific community into widely shared agreement, especially with regard to the implausibility of some logically possible alternatives. This seems to be the primary epistemic role of thought experiments. Consider, for example, the kinds of plausibility arguments that have been brought to bear on the various interpretations of quantum theory (e.g., those related to the measurement problem). These arguments go to the heart of conceptual issues that were central to the original development of the theory. Many of these issues were first raised by those scientists who made the greatest contributions to the development of quantum theory, in their attempts to get a conceptual hold on the theory and its implications.

Given any body of evidence, it is fairly easy to cook up a host of logically possible alternative hypotheses that make the evidence as probable as desired. In particular, it is easy to cook up hypotheses that logically entail any given body evidence, providing likelihood values equal to 1 for all the available evidence. Although most of these cooked up hypotheses will be laughably implausible, evidential likelihoods cannot rule them out. But, the only factors other than likelihoods that figure into the values of posterior probabilities for hypotheses are the values of their prior probabilities; so only prior probability assessments provide a place for the Bayesian logic to bring important plausibility considerations to bear. Thus, the Bayesian logic can only give implausible hypotheses their due via prior probability assessments.

It turns out that the mathematical structure of Bayesian inference makes prior probabilities especially well-suited to represent plausibility assessments among competing hypotheses. For, in the fully fleshed out account of evidential support for hypotheses (spelled out below), it will turn out that only ratios of prior probabilities for competing hypotheses, $P_{\alpha}[h_j \pmid b] / P_{\alpha}[h_i \pmid b]$, together with ratios of likelihoods, $P_{\alpha}[e \pmid h_j\cdot b\cdot c] / P_{\alpha}[e \pmid h_2\cdot b\cdot c]$, play essential roles. The ratio of prior probabilities is well-suited to represent how much more (or less) plausible hypothesis $h_j$ is than competing hypothesis $h_i$. Furthermore, the plausibility arguments on which such this comparative assessment is based may be explicitly stated within $b$. So, given that an inductive logic needs to incorporate well-considered plausibility assessments (e.g. in order to lay low wildly implausible alternative hypotheses), the comparative assessment of Bayesian prior probabilities seems well-suited to do the job.

Thus, although prior probabilities may be subjective in the sense that agents may disagree on the relative strengths of plausibility arguments, the priors used in scientific contexts need not represent mere subjective whims . Rather, the comparative strengths of the priors for hypotheses should be supported by arguments about how much more plausible one hypothesis is than another. The important role of plausibility assessments is captured by such received bits of scientific wisdom as the well-known scientific aphorism, extraordinary claims require extraordinary evidence . That is, it takes especially strong evidence, in the form of extremely high values for (ratios of) likelihoods, to overcome the extremely low pre-evidential plausibility values possessed by some hypotheses. In the next section we’ll see precisely how this idea works, and we’ll return to it again in Section 3.4 .

When sufficiently strong evidence becomes available, it turns out that the contributions of prior plausibility assessments to the values of posterior probabilities may be substantially “washed out”, overridden by the evidence. That is, provided the prior probability of a true hypothesis isn’t assessed to be too close to zero, the influence of the values of the prior probabilities will very probably fade away as evidence accumulates. In Section 4 we’ll see precisely how this kind of Bayesian convergence to the true hypothesis works. Thus, it turns out that prior plausibility assessments play their most important role when the distinguishing evidence represented by the likelihoods remains weak.

One more point before moving on to the logic of Bayes’ Theorem. Some Bayesian logicists have maintained that posterior probabilities of hypotheses should be determined by syntactic logical form alone. The idea is that the likelihoods might reasonably be specified in terms of syntactic logical form; so if syntactic form might be made to determine the values of prior probabilities as well, then inductive logic would be fully “formal” in the same way that deductive logic is “formal”. Keynes and Carnap tried to implement this idea through syntactic versions of the principle of indifference—the idea that syntactically similar hypotheses should be assigned the same prior probability values. Carnap showed how to carry out this project in detail, but only for extremely simple formal languages. Most logicians now take the project to have failed because of a fatal flaw with the whole idea that reasonable prior probabilities can be made to depend on logical form alone. Semantic content should matter. Goodmanian grue-predicates provide one way to illustrate this point. [ 8 ] Furthermore, as suggested earlier, for this idea to apply to the evidential support of real scientific theories, scientists would have to assess the prior probabilities of each alternative theory based only on its syntactic structure. That seems an unreasonable way to proceed. Are we to evaluate the prior probabilities of alternative theories of gravitation, or for alternative quantum theories, by exploring only their syntactic structures, with absolutely no regard for their content—with no regard for what they say about the world? This seems an extremely dubious approach to the evaluation of real scientific theories. Logical structure alone cannot, and should not suffice for determining reasonable prior probability values for real scientific theories. Moreover, real scientific hypotheses and theories are inevitably subject to plausibility considerations based on what they say about the world. Prior probabilities are well-suited to represent the comparative weight of plausibility considerations for alternative hypotheses. But no reasonable assessment of comparative plausibility can derive solely from the logical form of hypotheses.

We will return to a discussion of prior probabilities a bit later. Let’s now see how Bayesian logic combines likelihoods with prior probabilities to yield posterior probabilities for hypotheses.

Any probabilistic inductive logic that draws on the usual rules of probability theory to represent how evidence supports hypotheses must be a Bayesian inductive logic in the broad sense. For, Bayes’ Theorem follows directly from the usual axioms of probability theory. Its importance derives from the relationship it expresses between hypotheses and evidence. It shows how evidence, via the likelihoods, combines with prior probabilities to produce posterior probabilities for hypotheses. We now examine several forms of Bayes’ Theorem, each derivable from axioms 1–5 .

The simplest version of Bayes’ Theorem as it applies to evidence for a hypothesis goes like this:

Bayes’ Theorem: Simple Form

This equation expresses the posterior probability of hypothesis $h_i$ due to evidence $e$, $P_{\alpha}[h_i \pmid e]$, in terms of the likelihood of the evidence on that hypothesis, $P_{\alpha}[e \pmid h_i]$, the prior probability of the hypothesis , $P_{\alpha}[h_i]$, and the simple probability of the evidence , $P_{\alpha}[e]$. The factor $P_{\alpha}[e]$ is often called the expectedness of the evidence . Written this way, the theorem suppresses the experimental (or observational) conditions, $c$, and all background information and auxiliary hypotheses, $b$. As discussed earlier, both of these terms play an important role in logically connecting the hypothesis at issue, $h_i$, to the evidence $e$. In scientific contexts the objectivity of the likelihoods, $P_{\alpha}[e \pmid h_i\cdot b \cdot c]$, almost always depends on such terms. So, although the suppression of experimental (or observational) conditions and auxiliary hypotheses is a common practice in accounts of Bayesian inference, the treatment below, and throughout the remainder of this article will make the role of these terms explicit.

The subscript $\alpha$ on the evidential support function $P_{\alpha}$ is there to remind us that more than one such function exists. A host of distinct probability functions satisfy axioms 1–5 , so each of them satisfies Bayes’ Theorem. Some of these probability functions may provide a better fit with our intuitive conception of how the evidential support for hypotheses should work. Nevertheless, there are bound to be reasonable differences among Bayesian agents regarding to the initial plausibility of a hypothesis $h_i$. This diversity in initial plausibility assessments is represented by diverse values for prior probabilities for the hypothesis: $P_{\alpha}[h_i]$, $P_{\beta}[h_i]$, $P_{\gamma}[h_i]$, etc. This usually results in diverse values for posterior probabilities for hypotheses: $P_{\alpha}[h_i \pmid e]$, $P_{\beta}[h_i \pmid e]$, $P_{\gamma}[h_i \pmid e]$, etc. So it is important to keep the diversity among evidential support functions in mind.

Here is how the Simple Form of Bayes’ Theorem looks when terms for the experimental (or observational) conditions, $c$, and the background information and auxiliary hypotheses $b$ are made explicit:

Bayes’ Theorem: Simple Form with explicit Experimental Conditions, Background Information and Auxiliary Hypotheses

This version of the theorem determines the posterior probability of the hypothesis, $P_{\alpha}[h_i \pmid b\cdot c\cdot e]$, from the value of the likelihood of the evidence according to that hypothesis (taken together with background and auxiliaries and the experimental conditions), $P[e \pmid h_i\cdot b\cdot c]$, the value of the prior probability of the hypothesis (on background and auxiliaries), $P_{\alpha}[h_i \pmid b]$, and the value of the expectedness of the evidence (on background and auxiliaries and the experimental conditions), $P_{\alpha}[e \pmid b\cdot c]$. Notice that in the factor for the likelihood, $P[e \pmid h_i\cdot b\cdot c]$, the subscript $\alpha$ has been dropped. This marks the fact that in scientific contexts the likelihood of an evidential outcome $e$ on the hypothesis together with explicit background and auxiliary hypotheses and the description of the experimental conditions, $h_i\cdot b\cdot c$, is usually objectively determinate. This factor represents what the hypothesis (in conjunction with background and auxiliaries) objectively says about the likelihood of possible evidential outcomes of the experimental conditions. So, all reasonable support functions should agree on the values for likelihoods. (Section 5 will treat cases where the likelihoods may lack this kind of objectivity.)

This version of Bayes’ Theorem includes a term that represents the ratio of the likelihood of the experimental conditions on the hypothesis and background information (and auxiliaries) to the “likelihood” of the experimental conditions on the background (and auxiliaries) alone: $P_{\alpha}[c \pmid h_i\cdot b]/ P_{\alpha}[c \pmid b]$. Arguably the value of this term should be 1, or very nearly 1, since the truth of the hypothesis at issue should not significantly affect how likely it is that the experimental conditions are satisfied. If various alternative hypotheses assign significantly different likelihoods to the experimental conditions themselves, then such conditions should more properly be included as part of the evidential outcome $e$.

Both the prior probability of the hypothesis and the expectedness tend to be somewhat subjective factors in that various agents from the same scientific community may legitimately disagree on what values these factors should take. Bayesian logicians usually accept the apparent subjectivity of the prior probabilities of hypotheses, but find the subjectivity of the expectedness to be more troubling. This is due at least in part to the fact that in a Bayesian logic of evidential support the value of the expectedness cannot be determined independently of likelihoods and prior probabilities of hypotheses. That is, when, for each member of a collection of alternative hypotheses, the likelihood $P[e \pmid h_j\cdot b\cdot c]$ has an objective (or intersubjectively agreed) value, the expectedness is constrained by the following equation (where the sum ranges over a mutually exclusive and exhaustive collection of alternative hypotheses $\{h_1, h_2 , \ldots ,h_m , \ldots \}$, which may be finite or countably infinite):

This equation shows that the values for the prior probabilities together with the values of the likelihoods uniquely determine the value for the expectedness of the evidence . Furthermore, it implies that the value of the expectedness must lie between the largest and smallest of the various likelihood values implied by the alternative hypotheses. However, the precise value of the expectedness can only be calculated this way when every alternative to hypothesis $h_j$ is specified. In cases where some alternative hypotheses remain unspecified (or undiscovered), the value of the expectedness is constrained in principle by the totality of possible alternative hypotheses, but there is no way to figure out precisely what its value should be.

Troubles with determining a numerical value for the expectedness of the evidence may be circumvented by appealing to another form of Bayes’ Theorem, a ratio form that compares hypotheses one pair at a time:

Bayes’ Theorem: Ratio Form

The clause $P_{\alpha}[c \pmid h_j\cdot b] = P_{\alpha}[c \pmid h_i\cdot b]$ says that the experimental (or observation) condition described by $c$ is as likely on $(h_i\cdot b)$ as on $(h_j\cdot b)$ — i.e., the experimental or observation conditions are no more likely according to one hypothesis than according to the other. [ 9 ]

This Ratio Form of Bayes’ Theorem expresses how much more plausible, on the evidence, one hypothesis is than another. Notice that the likelihood ratios carry the full import of the evidence. The evidence influences the evaluation of hypotheses in no other way. The only other factor that influences the value of the ratio of posterior probabilities is the ratio of the prior probabilities. When the likelihoods are fully objective, any subjectivity that affects the ratio of posteriors can only arise via subjectivity in the ratio of the priors.

This version of Bayes’s Theorem shows that in order to evaluate the posterior probability ratios for pairs of hypotheses, the prior probabilities of hypotheses need not be evaluated absolutely; only their ratios are needed. That is, with regard to the priors, the Bayesian evaluation of hypotheses only relies on how much more plausible one hypothesis is than another (due to considerations expressed within b ). This kind of Bayesian evaluation of hypotheses is essentially comparative in that only ratios of likelihoods and ratios of prior probabilities are ever really needed for the assessment of scientific hypotheses. Furthermore, we will soon see that the absolute values of the posterior probabilities of hypotheses entirely derive from the posterior probability ratios provided by the Ratio Form of Bayes’ Theorem.

When the evidence consists of a collection of n distinct experiments or observations, we may explicitly represent this fact by replacing the term ‘$c$’ by the conjunction of experimental or observational conditions, $(c_1\cdot c_2\cdot \ldots \cdot c_n)$, and replacing the term ‘$e$’ by the conjunction of their respective outcomes, $(e_1\cdot e_2\cdot \ldots \cdot e_n)$. For notational convenience, let’s use the term ‘$c^n$’ to abbreviate the conjunction of n the experimental conditions, and we use the term ‘$e^n$’ to abbreviate the corresponding conjunction of n their respective outcomes. Relative to any given hypothesis $h$, the evidential outcomes of distinct experiments or observations will usually be probabilistically independent of one another, and also independent of the experimental conditions for one another. In that case we have:

When the Ratio Form of Bayes’ Theorem is extended to explicitly represent the evidence as consisting of a collection of n of distinct experiments (or observations) and their respective outcomes, it takes the following form.

Bayes’ Theorem: Ratio Form for a Collection of n Distinct Evidence Claims

Furthermore, when evidence claims are probabilistically independent of one another, we have

Let’s consider a simple example of how the Ratio Form of Bayes’ Theorem applies to a collection of independent evidential events. Suppose we possess a warped coin and want to determine its propensity for heads when tossed in the usual way. Consider two hypotheses, $h_{[p]}$ and $h_{[q]}$, which say that the propensities for the coin to come up heads on the usual kinds of tosses are $p$ and $q$, respectively. Let $c^n$ report that the coin is tossed n times in the normal way, and let $e^n$ report that precisely m occurrences of heads has resulted. Supposing that the outcomes of such tosses are probabilistically independent (asserted by $b$), the respective likelihoods take the binomial form

with $r$ standing in for $p$ and for $q$, respectively. Then, Equation 9** yields the following formula, where the likelihood ratio is the ratio of the respective binomial terms:

When, for instance, the coin is tossed $n = 100$ times and comes up heads $m = 72$ times, the evidence for hypothesis $h_{[1/2]}$ as compared to $h_{[3/4]}$ is given by the likelihood ratio

In that case, even if the prior plausibility considerations (expressed within $b$) make it 100 times more plausible that the coin is fair than that it is warped towards heads with propensity 3/4 — i.e., even if $P_{\alpha}[h_{[1/2]} \pmid b] / P_{\alpha}[h_{[3/4]} \pmid b] = 100$ — the evidence provided by these tosses makes the posterior plausibility that the coin is fair only about 6/1000 ths as plausible as the hypothesis that it is warped towards heads with propensity 3/4 :

Thus, such evidence strongly refutes the “fairness hypothesis” relative to the “3/4- heads hypothesis”, provided the assessment of prior prior plausibilities doesn’t make the latter hypothesis too extremely implausible to begin with. Notice, however, that strong refutation is not absolute refutation . Additional evidence could reverse this trend towards the refutation of the fairness hypothesis .

This example employs repetitions of the same kind of experiment—repeated tosses of a coin. But the point holds more generally. If, as the evidence increases, the likelihood ratios

approach 0, then the Ratio Forms of Bayes’ Theorem, Equations $9*)$ and $9**)$, show that the posterior probability of $h_j$ must approach 0 as well, since

Such evidence comes to strongly refute $h_j$, with little regard for its prior plausibility value. Indeed, Bayesian induction turns out to be a version of eliminative induction , and Equation $9*$ and $9**$ begin to illustrate this. For, suppose that $h_i$ is the true hypothesis, and consider what happens to each of its false competitors, $h_j$. If enough evidence becomes available to drive each of the likelihood ratios

toward 0 (as n increases), then Equation $9*$ says that each false $h_j$ will become effectively refuted — each of their posterior probabilities will approaches 0 (as n increases). As a result, the posterior probability of $h_i$ must approach 1. The next two equations show precisely how this works.

If we sum the ratio versions of Bayes’ Theorem in Equation $9*$ over all alternatives to hypothesis $h_i$ (including the catch-all alternative $h_K$, if appropriate), we get the Odds Form of Bayes’ Theorem. By definition, the odds against a statement $A$ given $B$ is related to the probability of $A$ given $B$ as follows:

This notion of odds gives rise to the following version of Bayes’ Theorem:

Bayes’ Theorem: Odds Form

where the factor following the ‘ + ’ sign is only required in cases where a catch-all alternative hypothesis, $h_K$, is needed.

Recall that when we have a finite collection of concrete alternative hypotheses available, $\{h_1, h_2 , \ldots ,h_m\}$, but where this set of alternatives is not exhaustive (where additional, unarticulated, undiscovered alternative hypotheses may exist), the catch-all alternative hypothesis $h_K$ is just the denial of each of the concrete alternatives, $({\nsim}h_1\cdot{\nsim}h_2\cdot \ldots \cdot{\nsim}h_m)$. Generally, the likelihood of evidence claims relative to a catch-all hypothesis will not enjoy the same kind of objectivity possessed by the likelihoods for concrete alternative hypotheses. So, we leave the subscript $\alpha$ attached to the likelihood for the catch-all hypothesis to indicate this lack of objectivity.

Although the catch-all hypothesis may lack objective likelihoods, the influence of the catch-all term in Bayes’ Theorem diminishes as additional concrete hypotheses are articulated. That is, as new hypotheses are discovered they are “peeled off” of the catch-all. So, when a new hypothesis $h_{m+1}$ is formulated and made explicit, the old catch-all hypothesis $h_K$ is replaced by a new catch-all, $h_{K*}$, of form $({\nsim}h_1\cdot \cdot{\nsim}h_2\cdot \ldots \cdot{\nsim}h_{m}\cdot{\nsim}h_{m+1})$; and the prior probability for the new catch-all hypothesis is gotten by diminishing the prior of the old catch-all: $P_{\alpha}[h_{K*} \pmid b] = P_{\alpha}[h_K \pmid b] - P_{\alpha}[h_{m+1} \pmid b]$. Thus, the influence of the catch-all term should diminish towards 0 as new alternative hypotheses are made explicit. [ 10 ]

If increasing evidence drives towards 0 the likelihood ratios comparing each competitor $h_j$ with hypothesis $h_i$, then the odds against $h_i$, $\Omega_{\alpha}[{\nsim}h_i \pmid b\cdot c^{n}\cdot e^{n}]$, will approach 0 (provided that priors of catch-all terms, if needed, approach 0 as well, as new alternative hypotheses are made explicit and peeled off). And, as $\Omega_{\alpha}[{\nsim}h_i \pmid b\cdot c^{n}\cdot e^{n}]$ approaches 0, the posterior probability of $h_i$ goes to 1. This derives from the fact that the odds against $h_i$ is related to and its posterior probability by the following formula:

Bayes’ Theorem: General Probabilistic Form

The odds against a hypothesis depends only on the values of ratios of posterior probabilities , which entirely derive from the Ratio Form of Bayes’ Theorem. Thus, we see that the individual value of the posterior probability of a hypothesis depends only on the ratios of posterior probabilities , which come from the Ratio Form of Bayes’ Theorem. Thus, the Ratio Form of Bayes’ Theorem captures all the essential features of the Bayesian evaluation of hypothesis. It shows how the impact of evidence (in the form of likelihood ratios) combines with comparative plausibility assessments of hypotheses (in the form of ratios of prior probabilities) to provide a net assessment of the extent to which hypotheses are refuted or supported via contests with their rivals.

There is a result, a kind of Bayesian Convergence Theorem , that shows that if $h_i$ (together with $b\cdot c^n)$ is true, then the likelihood ratios

comparing evidentially distinguishable alternative hypothesis $h_j$ to $h_i$ will very probably approach 0 as evidence accumulates (i.e., as n increases). Let’s call this result the Likelihood Ratio Convergence Theorem . When this theorem applies, Equation $9^*$ shows that the posterior probability of a false competitor $h_j$ will very probably approach 0 as evidence accumulates, regardless of the value of its prior probability $P_{\alpha}[h_j \pmid b]$. As this happens to each of $h_i$’s false competitors, Equations 10 and 11 say that the posterior probability of the true hypothesis, $h_i$, will approach 1 as evidence increases. [ 11 ] Thus, Bayesian induction is at bottom a version of induction by elimination , where the elimination of alternatives comes by way of likelihood ratios approaching 0 as evidence accumulates. Thus, when the Likelihood Ratio Convergence Theorem applies, the Criterion of Adequacy for an Inductive Logic described at the beginning of this article will be satisfied: As evidence accumulates, the degree to which the collection of true evidence statements comes to support a hypothesis, as measured by the logic, should very probably come to indicate that false hypotheses are probably false and that true hypotheses are probably true. We will examine this Likelihood Ratio Convergence Theorem in Section 4 . [ 12 ]

A view called Likelihoodism relies on likelihood ratios in much the same way as the Bayesian logic articulated above. However, Likelihoodism attempts to avoid the use of prior probabilities. For an account of this alternative view, see the supplement Likelihood Ratios, Likelihoodism, and the Law of Likelihood . For more discussion of Bayes’ Theorem and its application, see the entries on Bayes’ Theorem and on Bayesian Epistemology in this Encyclopedia .

Given that a scientific community should largely agree on the values of the likelihoods, any significant disagreement among them with regard to the values of posterior probabilities of hypotheses should derive from disagreements over their assessments of values for the prior probabilities of those hypotheses. We saw in Section 3.3 that the Bayesian logic of evidential support need only rely on assessments of ratios of prior probabilities —on how much more plausible one hypothesis is than another. Thus, the logic of evidential support only requires that scientists can assess the comparative plausibilities of various hypotheses. Presumably, in scientific contexts the comparative plausibility values for hypotheses should depend on explicit plausibility arguments, not merely on privately held opinions. (Formally, the logic may represent comparative plausibility arguments by explicit statements expressed within $b$.) It would be highly unscientific for a member of the scientific community to disregard or dismiss a hypothesis that other members take to be a reasonable proposal with only the comment, “don’t ask me to give my reasons, it’s just my opinion”. Even so, agents may be unable to specify precisely how much more strongly the available plausibility arguments support a hypothesis over an alternative; so prior probability ratios for hypotheses may be vague. Furthermore, agents in a scientific community may disagree about how strongly the available plausibility arguments support a hypothesis over a rival hypothesis; so prior probability ratios may be somewhat diverse as well.

Both the vagueness of comparative plausibilities assessments for individual agents and the diversity of such assessments among the community of agents can be represented formally by sets of support functions, $\{P_{\alpha}, P_{\beta}, \ldots \}$, that agree on the values for the likelihoods but encompass a range of values for the (ratios of) prior probabilities of hypotheses. Vagueness and diversity are somewhat different issues, but they may be represented in much the same way. Let’s briefly consider each in turn.

Assessments of the prior plausibilities of hypotheses will often be vague—not subject to the kind of precise quantitative treatment that a Bayesian version of probabilistic inductive logic may seem to require for prior probabilities. So, it may seem that the kind of assessment of prior probabilities required to get the Bayesian algorithm going cannot be accomplished in practice. To see how Bayesian inductivists address this worry, first recall the Ratio Form of Bayes’ Theorem, Equation $9^*$.

Recall that this Ratio Form of the theorem captures the essential features of the logic of evidential support, even though it only provides a value for the ratio of the posterior probabilities. Notice that the ratio form of the theorem easily accommodates situations where we don’t have precise numerical values for prior probabilities. It only depends on our ability to assess how much more or less plausible alternative hypothesis $h_j$ is than hypothesis $h_i$—only the value of the ratio $P_{\alpha}[h_j \pmid b] / P_{\alpha}[h_i \pmid b]$ need be assessed; the values of the individual prior probabilities are not needed. Such comparative plausibilities are much easier to assess than specific numerical values for the prior probabilities of individual hypotheses. When combined with the ratio of likelihoods , this ratio of priors suffices to yield an assessment of the ratio of posterior plausibilities ,

Although such posterior ratios don’t supply values for the posterior probabilities of individual hypotheses, they place a crucial constraint on the posterior support of hypothesis $h_j$, since

This Ratio Form of Bayes’ Theorem tolerates a good deal of vagueness or imprecision in assessments of the ratios of prior probabilities. In practice one need only assess bounds for these prior plausibility ratios to achieve meaningful results. Given a prior ratio in a specific interval,

a likelihood ratio

results in a posterior support ratio in the interval

(Technically each probabilistic support function assigns a specific numerical value to each pair of sentences; so when we write an inequality like

we are really referring to a set of probability functions $P_{\alpha}$, a vagueness set , for which the inequality holds. Thus, technically, the Bayesian logic employs sets of probabilistic support functions to represent the vagueness in comparative plausibility values for hypotheses.)

Observe that if the likelihood ratio values $\LR^n$ approach 0 as the amount of evidence $e^n$ increases, the interval of values for the posterior probability ratio must become tighter as the upper bound ($\LR^n\times r)$ approaches 0. Furthermore, the absolute degree of support for $h_j$, $P_{\alpha}[h_j \pmid b\cdot c^{n}\cdot e^{n}]$, must also approach 0.

This observation is really useful. For, it can be shown that when $h_{i}\cdot b\cdot c^{n}$ is true and $h_j$ is empirically distinct from $h_i$, the continual pursuit of evidence is very likely to result in evidential outcomes $e^n$ that (as n increases) yield values of likelihood ratios $P[e^n \pmid h_{j}\cdot b\cdot c^{n}] / P[e^n \pmid h_{i}\cdot b\cdot c^{n}]$ that approach 0 as the amount of evidence increases. This result, called the Likelihood Ratio Convergence Theorem , will be investigated in more detail in Section 4 . When that kind of convergence towards 0 for likelihood ratios occurs, the upper bound on the posterior probability ratio also approaches 0, driving the posterior probability of $h_j$ to approach 0 as well, effectively refuting hypothesis $h_j$. Thus, false competitors of a true hypothesis will effectively be eliminated by increasing evidence. As this happens, Equations 9* through 11 show that the posterior probability $P_{\alpha}[h_i \pmid b\cdot c^{n}\cdot e^{n}]$ of the true hypothesis $h_i$ approaches 1.

Thus, Bayesian logic of inductive support for hypotheses is a form of eliminative induction, where the evidence effectively refutes false alternatives to the true hypothesis. Because of its eliminative nature, the Bayesian logic of evidential support doesn’t require precise values for prior probabilities. It only needs to draw on bounds on the values of comparative plausibility ratios, and these bounds only play a significant role while evidence remains fairly weak. If the true hypothesis is assessed to be comparatively plausible (due to plausibility arguments contained in b ), then plausibility assessments give it a leg-up over alternatives. If the true hypothesis is assessed to be comparatively implausible, the plausibility assessments merely slow down the rate at which it comes to dominate its rivals, reflecting the idea that extraordinary hypotheses require extraordinary evidence (or an extraordinary accumulation of evidence) to overcome their initial implausibilities. Thus, as evidence accumulates, the agent’s vague initial plausibility assessments transform into quite sharp posterior probabilities that indicate their strong refutation or support by the evidence.

When the various agents in a community may widely disagree over the non-evidential plausibilities of hypotheses, the Bayesian logic of evidential support may represent this kind of diversity across the community of agents as a collection of the agents’ vagueness sets of support functions. Let’s call such a collection of support functions a diversity set . That is, a diversity set is just a set of support functions $P_{\alpha}$ that cover the ranges of values for comparative plausibility assessments for pairs of competing hypotheses

as assessed by the scientific community. But, once again, if accumulating evidence drives the likelihood ratios comparing various alternative hypotheses to the true hypothesis towards 0, the range of support functions in a diversity set will come to near agreement, near 0, on the values for posterior probabilities of false competitors of the true hypothesis. So, not only does such evidence firm up each agent’s vague initial plausibility assessment, it also brings the whole community into agreement on the near refutation of empirically distinct competitors of a true hypothesis. As this happens, the posterior probability of the true hypothesis may approach 1. The Likelihood Ratio Convergence Theorem implies that this kind of convergence to the truth should very probably happen, provided that the true hypothesis is empirically distinct enough from its rivals.

One more point about prior probabilities and Bayesian convergence should be mentioned before proceeding to Section 4 . Some subjectivist versions of Bayesian induction seem to suggest that an agent’s prior plausibility assessments for hypotheses should stay fixed once-and-for-all, and that all plausibility updating should be brought about via the likelihoods in accord with Bayes’ Theorem. Critics argue that this is unreasonable. The members of a scientific community may quite legitimately revise their (comparative) prior plausibility assessments for hypotheses from time to time as they rethink plausibility arguments and bring new considerations to bear. This seems a natural part of the conceptual development of a science. It turns out that such reassessments of the comparative plausibilities of hypotheses poses no difficulty for the probabilistic inductive logic discussed here. Such reassessments may be represented by the addition or modification of explicit statements that modify the background information b . Such reassessments may result in (non-Bayesian) transitions to new vagueness sets for individual agents and new diversity sets for the community. The logic of Bayesian induction (as described here) has nothing to say about what values the prior plausibility assessments for hypotheses should have; and it places no restrictions on how they might change over time. Provided that the series of reassessments of (comparative) prior plausibilities doesn’t happen to diminish the (comparative) prior plausibility value of the true hypothesis towards zero (or, at least, doesn’t do so too quickly), the Likelihood Ratio Convergence Theorem implies that the evidence will very probably bring the posterior probabilities of empirically distinct rivals of the true hypothesis to approach 0 via decreasing likelihood ratios; and as this happens, the posterior probability of the true hypothesis will head towards 1.

(Those interested in a Bayesian account of Enumerative Induction and the estimation of values for relative frequencies of attributes in populations should see the supplement, Enumerative Inductions: Bayesian Estimation and Convergence .)

4. The Likelihood Ratio Convergence Theorem

In this section we will investigate the Likelihood Ratio Convergence Theorem . This theorem shows that under certain reasonable conditions, when hypothesis $h_i$ (in conjunction with auxiliaries in b ) is true and an alternative hypothesis $h_j$ is empirically distinct from $h_i$ on some possible outcomes of experiments or observations described by conditions $c_k$, then it is very likely that a long enough sequence of such experiments and observations c$^n$ will produce a sequence of outcomes $e^n$ that yields likelihood ratios $P[e^n \pmid h_{j}\cdot b\cdot c^{n}] / P[e^n \pmid h_{i}\cdot b\cdot c^{n}]$ that approach 0, favoring $h_i$ over $h_j$, as evidence accumulates (i.e., as n increases). This theorem places an explicit lower bound on the “rate of probable convergence” of these likelihood ratios towards 0. That is, it puts a lower bound on how likely it is, if $h_i$ is true, that a stream of outcomes will occur that yields likelihood ratio values against $h_j$ as compared to $h_i$ that lie within any specified small distance above 0.

The theorem itself does not require the full apparatus of Bayesian probability functions. It draws only on likelihoods. Neither the statement of the theorem nor its proof employ prior probabilities of any kind. So even likelihoodists , who eschew the use of Bayesian prior probabilities, may embrace this result. Given the forms of Bayes’ Theorem, 9*-11 from the previous section, the Likelihood Ratio Convergence Theorem further implies the likely convergence to 0 of the posterior probabilities of false competitors of a true hypothesis. That is, when the ratios $P[e^n \pmid h_{j}\cdot b\cdot c^{n}] / P[e^n \pmid h_{i}\cdot b\cdot c^{n}]$ approach 0 for increasing n , the Ratio Form of Bayes’ Theorem, Equation 9* , says that the posterior probability of $h_j$ must also approach 0 as evidence accumulates, regardless of the value of its prior probability. So, support functions in collections representing vague prior plausibilities for an individual agent (i.e., a vagueness set) and representing the diverse range of priors for a community of agents (i.e., a diversity set) will come to agree on the near 0 posterior probability of empirically distinct false rivals of a true hypothesis. And as the posterior probabilities of false competitors fall, the posterior probability of the true hypothesis heads towards 1. Thus, the theorem establishes that the inductive logic of probabilistic support functions satisfies the Criterion of Adequacy (CoA) suggested at the beginning of this article.

The Likelihood Ratio Convergence Theorem merely provides some sufficient conditions for probable convergence. But likelihood ratios may well converge towards 0 (in the way described by the theorem) even when the antecedent conditions of the theorem are not satisfied. This theorem overcomes many of the objections raised by critics of Bayesian convergence results. First, this theorem does not employ second-order probabilities ; it says noting about the probability of a probability. It only concerns the probability of a particular disjunctive sentence that expresses a disjunction of various possible sequences of experimental or observational outcomes. The theorem does not require evidence to consist of sequences of events that, according to the hypothesis, are identically distributed (like repeated tosses of a die). The result is most easily expressed in cases where the individual outcomes of a sequence of experiments or observations are probabilistically independent, given each hypothesis. So that is the version that will be presented in this section. However, a version of the theorem also holds when the individual outcomes of the evidence stream are not probabilistically independent, given the hypotheses. (This more general version of the theorem will be presented in a supplement on the Probabilistic Refutation Theorem , below, where the proof of both versions is provided.) In addition, this result does not rely on supposing that the probability functions involved are countably additive . Furthermore, the explicit lower bounds on the rate of convergence provided by this result means that there is no need to wait for the infinitely long run before convergence occurs (as some critics seem to think).

It is sometimes claimed that Bayesian convergence results only work when an agent locks in values for the prior probabilities of hypotheses once-and-for-all, and then updates posterior probabilities from there only by conditioning on evidence via Bayes Theorem. The Likelihood Ratio Convergence Theorem , however, applies even if agents revise their prior probability assessments over time. Such non-Bayesian shifts from one support function (or vagueness set) to another may arise from new plausibility arguments or from reassessments of the strengths of old ones. The Likelihood Ratio Convergence Theorem itself only involves the values of likelihoods. So, provided such reassessments don’t push the prior probability of the true hypothesis towards 0 too rapidly , the theorem implies that the posterior probabilities of each empirically distinct false competitor will very probably approach 0 as evidence increases. [ 13 ]

To specify the details of the Likelihood Ratio Convergence Theorem we’ll need a few additional notational conventions and definitions. Here they are.

For a given sequence of n experiments or observations $c^n$, consider the set of those possible sequences of outcomes that would result in likelihood ratios for $h_j$ over $h_i$ that are less than some chosen small number $\varepsilon \gt 0$. This set is represented by the expression,

Placing the disjunction symbol ‘$\vee$’ in front of this expression yields an expression,

that we’ll use to represent the disjunction of all outcome sequences $e^n$ in this set. So,

is just a particular sentence that says, in effect, “one of the sequences of outcomes of the first n experiments or observations will occur that makes the likelihood ratio for $h_j$ over $h_i$ less than $\varepsilon$”.

The Likelihood Ratio Convergence Theorem says that under certain conditions (covered in detail below), the likelihood of a disjunctive sentence of this sort, given that ‘$h_{i}\cdot b\cdot c^{n}$’ is true,

must be at least $1-(\psi /n)$, for some explicitly calculable term $\psi$. Thus, the true hypothesis $h_i$ probabilistically implies that as the amount of evidence, n , increases, it becomes highly likely (as close to 1 as you please) that one of the outcome sequences $e^n$ will occur that yields a likelihood ratio $P[e^n \pmid h_{j}\cdot b\cdot c^{n}] / P[e^n \pmid h_{i}\cdot b\cdot c^{n}]$ less than $\varepsilon$; and this holds for any specific value of $\varepsilon$ you may choose. As this happens, the posterior probability of $h_i$’s false competitor, $h_j$, must approach 0, as required by the Ratio Form of Bayes’ Theorem, Equation 9* .

The term $\psi$ in the lower bound of this probability depends on a measure of the empirical distinctness of the two hypotheses $h_j$ and $h_i$ for the proposed sequence of experiments and observations $c^n$. To specify this measure we need to contemplate the collection of possible outcomes of each experiment or observation. So, consider some sequence of experimental or observational conditions described by sentences $c_1,c_2 ,\ldots ,c_n$. Corresponding to each condition $c_k$ there will be some range of possible alternative outcomes. Let $O_{k} = \{o_{k1},o_{k2},\ldots ,o_{kw}\}$ be a set of statements describing the alternative possible outcomes for condition $c_k$. (The number of alternative outcomes will usually differ for distinct experiments among those in the sequence $c_1 ,\ldots ,c_n$; so, the value of w may depend on $c_k$.) For each hypothesis $h_j$, the alternative outcomes of $c_k$ in $O_k$ are mutually exclusive and exhaustive, so we have:

We now let expressions of form ‘$e_k$’ act as variables that range over the possible outcomes of condition $c_k$—i.e., $e_k$ ranges over the members of $O_k$. As before, ‘$c^n$’ denotes the conjunction of the first n test conditions, $(c_1\cdot c_2\cdot \ldots \cdot c_n)$, and ‘$e^n$’ represents possible sequences of corresponding outcomes, $(e_1\cdot e_2\cdot \ldots \cdot e_n)$. Let’s use the expression ‘ E$^n$ ’ to represent the set of all possible outcome sequences that may result from the sequence of conditions c$^n$ . So, for each hypothesis $h_j$ (including $h_i)$, $\sum_{e^n\in E^n} P[e^n \pmid h_{j}\cdot b\cdot c^{n}] = 1$.

Everything introduced in this subsection is mere notational convention. No substantive suppositions (other than the axioms of probability theory) have yet been introduced. The version of the Likelihood Ratio Convergence Theorem I’ll present below does, however, draw on one substantive supposition, although a rather weak one. The next subsection will discuss that supposition in detail.

In most scientific contexts the outcomes in a stream of experiments or observations are probabilistically independent of one another relative to each hypothesis under consideration, or can at least be divided up into probabilistically independent parts. For our purposes probabilistic independence of evidential outcomes on a hypothesis divides neatly into two types.

Definition: Independent Evidence Conditions :

A sequence of outcomes $e^k$ is condition-independent of a condition for an additional experiment or observation $c_{k+1}$, given $h\cdot b$ together with its own conditions $c^k$, if and only if \[ P[e^k \pmid h\cdot b\cdot c^{k }\cdot c_{ k+1}] = P[e^k \pmid h\cdot b\cdot c^k] . \]
An individual outcome $e_k$ is result-independent of a sequence of other observations and their outcomes $(c^{k-1}\cdot e^{k-1})$, given $h\cdot b$ and its own condition $c_k$, if and only if \[ P[e_k \pmid h\cdot b\cdot c_k\cdot(c^{k-1 }\cdot e^{ k-1})] = P[e_k \pmid h\cdot b\cdot c_k] . \]

When these two conditions hold, the likelihood for an evidence sequence may be decomposed into the product of the likelihoods for individual experiments or observations. To see how the two independence conditions affect the decomposition, first consider the following formula, which holds even when neither independence condition is satisfied:

When condition-independence holds, the likelihood of the whole evidence stream parses into a product of likelihoods that probabilistically depend on only past observation conditions and their outcomes. They do not depend on the conditions for other experiments whose outcomes are not yet specified. Here is the formula:

Finally, whenever both independence conditions are satisfied we have the following relationship between the likelihood of the evidence stream and the likelihoods of individual experiments or observations:

(For proofs of Equations 12–14 see the supplement Immediate Consequences of Independent Evidence Conditions .)

In scientific contexts the evidence can almost always be divided into parts that satisfy both clauses of the Independent Evidence Condition with respect to each alternative hypothesis. To see why, let us consider each independence condition more carefully.

Condition-independence says that the mere addition of a new observation condition $c_{k+1}$, without specifying one of its outcomes , does not alter the likelihood of the outcomes $e^k$ of other experiments $c^k$. To appreciate the significance of this condition, imagine what it would be like if it were violated. Suppose hypothesis $h_j$ is some statistical theory, say, for example, a quantum theory of superconductivity. The conditions expressed in $c^k$ describe a number of experimental setups, perhaps conducted in numerous labs throughout the world, that test a variety of aspects of the theory (e.g., experiments that test electrical conductivity in different materials at a range of temperatures). An outcome sequence $e^k$ describes the results of these experiments. The violation of condition-independence would mean that merely adding to $h_{j}\cdot b\cdot c^{k}$ a statement $c_{k+1}$ describing how an additional experiment has been set up, but with no mention of its outcome, changes how likely the evidence sequence $e^k$ is taken to be. What $(h_j\cdot b)$ says via likelihoods about the outcomes $e^k$ of experiments $c^k$ differs as a result of merely supplying a description of another experimental arrangement, $c_{k+1}$. Condition-independence , when it holds, rules out such strange effects.

Result-independence says that the description of previous test conditions together with their outcomes is irrelevant to the likelihoods of outcomes for additional experiments. If this condition were widely violated, then in order to specify the most informed likelihoods for a given hypothesis one would need to include information about volumes of past observations and their outcomes. What a hypothesis says about future cases would depend on how past cases have gone. Such dependence had better not happen on a large scale. Otherwise, the hypothesis would be fairly useless, since its empirical import in each specific case would depend on taking into account volumes of past observational and experimental results. However, even if such dependencies occur, provided they are not too pervasive, result-independence can be accommodated rather easily by packaging each collection of result-dependent data together, treating it like a single extended experiment or observation. The result-independence condition will then be satisfied by letting each term ‘$c_k$’ in the statement of the independence condition represent a conjunction of test conditions for a collection of result-dependent tests, and by letting each term ‘$e_k$’ (and each term ‘$o_{ku}$’) stand for a conjunction of the corresponding result-dependent outcomes. Thus, by packaging result-dependent data together in this way, the result-independence condition is satisfied by those (conjunctive) statements that describe the separate, result-independent chunks. [ 14 ]

The version of the Likelihood Ratio Convergence Theorem we will examine depends only on the Independent Evidence Conditions (together with the axioms of probability theory). It draws on no other assumptions. Indeed, an even more general version of the theorem can be established, a version that draws on neither of the Independent Evidence Conditions . However, the Independent Evidence Conditions will be satisfied in almost all scientific contexts, so little will be lost by assuming them. (And the presentation will run more smoothly if we side-step the added complications needed to explain the more general result.)

From this point on, let us assume that the following versions of the Independent Evidence Conditions hold.

Assumption: Independent Evidence Assumptions . For each hypothesis h and background b under consideration, we assume that the experiments and observations can be packaged into condition statements, $c_1 ,\ldots ,c_k, c_{k+1},\ldots$, and possible outcomes in a way that satisfies the following conditions:

Each sequence of possible outcomes $e^k$ of a sequence of conditions $c^k$ is condition-independent of additional conditions $c_{k+1}$—i.e., \[P[e^k \pmid h\cdot b\cdot c^{k}\cdot c_{k+1}] = P[e^k \pmid h\cdot b\cdot c^k].\]
Each possible outcome $e_k$ of condition $c_k$ is result-independent of sequences of other observations and possible outcomes $(c^{k-1}\cdot e^{k-1})$—i.e., \[P[e_k \pmid h\cdot b\cdot c_k\cdot(c^{k-1}\cdot e^{k-1})] = P[e_k \pmid h\cdot b\cdot c_k].\]

We now have all that is needed to begin to state the Likelihood Ratio Convergence Theorem .

The Likelihood Ratio Convergence Theorem comes in two parts. The first part applies only to those experiments or observations $c_k$ within the total evidence stream $c^n$ for which some of the possible outcomes have 0 likelihood of occurring according to hypothesis $h_j$ but have non-0 likelihood of occurring according to $h_i$. Such outcomes are highly desirable. If they occur, the likelihood ratio comparing $h_j$ to $h_i$ will become 0, and $h_j$ will be falsified . So-called crucial experiments are a special case of this, where for at least one possible outcome $o_{ku}$, $P[o_{ku} \pmid h_{i}\cdot b\cdot c_{k}] = 1$ and $P[o_{ku} \pmid h_{j}\cdot b\cdot c_{k}] = 0$. In the more general case $h_i$ together with b says that one of the outcomes of $c_k$ is at least minimally probable, whereas $h_j$ says that this outcome is impossible—i.e., $P[o_{ku} \pmid h_{i}\cdot b\cdot c_{k}] \gt 0$ and $P[o_{ku} \pmid h_{j}\cdot b\cdot c_{k}] = 0$. It will be convenient to define a term for this situation.

Definition: Full Outcome Compatibility. Let’s call $h_j$ fully outcome-compatible with $h_i$ on experiment or observation $c_k$ just when , for each of its possible outcomes $e_k$, if $P[e_k \pmid h_{i}\cdot b\cdot c_{k}] \gt 0$, then $P[e_k \pmid h_{j}\cdot b\cdot c_{k}] \gt 0$. Equivalently, $h_j$ is fails to be fully outcome-compatible with $h_i$ on experiment or observation $c_k$ just when , for at least one of its possible outcomes $e_k$, $P[e_k \pmid h_{i}\cdot b\cdot c_{k}] \gt 0$ but $P[e_k \pmid h_{j}\cdot b\cdot c_{k}] = 0$.

The first part of the Likelihood Ratio Convergence Theorem applies to that part of the total stream of evidence (i.e., that subsequence of the total evidence stream) on which hypothesis $h_j$ fails to be fully outcome-compatible with hypothesis $h_i$; the second part of the theorem applies to the remaining part of the total stream of evidence, that subsequence of the total evidence stream on which $h_j$ is fully outcome-compatible with $h_i$. It turns out that these two kinds of cases must be treated differently. (This is due to the way in which the expected information content for empirically distinguishing between the two hypotheses will be measured for experiments and observations that are fully outcome compatible ; this measure of information content blows up (becomes infinite) for experiments and observations that fail to be fully outcome compatible ). Thus, the following part of the convergence theorem applies to just that part of the total stream of evidence that consists of experiments and observations that fail to be fully outcome compatible for the pair of hypotheses involved. Here, then, is the first part of the convergence theorem.

Likelihood Ratio Convergence Theorem 1—The Falsification Theorem: Suppose that the total stream of evidence $c^n$ contains precisely m experiments or observations on which $h_j$ fails to be fully outcome-compatible with $h_i$. And suppose that the Independent Evidence Conditions hold for evidence stream $c^n$ with respect to each of these two hypotheses. Furthermore, suppose there is a lower bound $\delta \gt 0$ such that for each $c_k$ on which $h_j$ fails to be fully outcome-compatible with $h_i$,

—i.e., $h_i$ together with $b\cdot c_k$ says , with likelihood at least as large as $\delta$, that one of the outcomes will occur that $h_j$ says cannot occur. Then,

which approaches 1 for large m . (For proof see Proof of the Falsification Theorem .)

In other words, we only suppose that for each of m observations, $c_k, h_i$ says observation $c_k$ has at least a small likelihood $\delta$ of producing one of the outcomes $o_{ku}$ that $h_j$ says is impossible. If the number m of such experiments or observations is large enough (or if the lower bound $\delta$ on the likelihoods of getting such outcomes is large enough), and if $h_i$ (together with $b\cdot c^n)$ is true, then it is highly likely that one of the outcomes held to be impossible by $h_j$ will actually occur. If one of these outcomes does occur, then the likelihood ratio for $h_j$ as compared to over $h_i$ will become 0. According to Bayes’ Theorem, when this happen, $h_j$ is absolutely refuted by the evidence—its posterior probability becomes 0.

The Falsification Theorem is quite commonsensical. First, notice that if there is a crucial experiment in the evidence stream, the theorem is completely obvious. That is, suppose for the specific experiment $c_k$ (in evidence stream $c^n)$ there are two incompatible possible outcomes $o_{kv}$ and $o_{ku}$ such that $P[o_{kv} \pmid h_{j}\cdot b\cdot c_{k}] = 1$ and $P[o_{ku} \pmid h_{i}\cdot b\cdot c_{k}] = 1$. Then, clearly, $P[\vee \{ o_{ku}: P[o_{ku} \pmid h_{j}\cdot b\cdot c_{k}] = 0\} \pmid h_{i}\cdot b\cdot c_{k}] = 1$, since $o_{ku}$ is one of the $o_{ku}$ such that $P[o_{ku} \pmid h_{j}\cdot b\cdot c_{k}] = 0$. So, where a crucial experiment is available, the theorem applies with $m = 1$ and $\delta = 1$.

The theorem is equally commonsensical for cases where no crucial experiment is available. To see what it says in such cases, consider an example. Let $h_i$ be some theory that implies a specific rate of proton decay, but a rate so low that there is only a very small probability that any particular proton will decay in a given year. Consider an alternative theory $h_j$ that implies that protons never decay. If $h_i$ is true, then for a persistent enough sequence of observations (i.e., if proper detectors can keep trillions of protons under observation for long enough), eventually a proton decay will almost surely be detected. When this happens, the likelihood ratio becomes 0. Thus, the posterior probability of $h_j$ becomes 0.

It is instructive to plug some specific values into the formula given by the Falsification Theorem, to see what the convergence rate might look like. For example, the theorem tells us that if we compare any pair of hypotheses $h_i$ and $h_j$ on an evidence stream $c^n$ that contains at least $m = 19$ observations or experiments, where each has a likelihood $\delta \ge .10$ of yielding a falsifying outcome , then the likelihood (on $h_{i}\cdot b\cdot c^{n})$ of obtaining an outcome sequence $e^n$ that yields likelihood-ratio

will be at least as large as $(1 - (1-.1)^{19}) = .865$. (The reader is invited to try other values of $\delta$ and m .)

A comment about the need for and usefulness of such convergence theorems is in order, now that we’ve seen one. Given some specific pair of scientific hypotheses $h_i$ and $h_j$ one may directly compute the likelihood, given $(h_{i}\cdot b\cdot c^{n})$, that a proposed sequence of experiments or observations $c^n$ will result in one of the sequences of outcomes that would yield low likelihood ratios. So, given a specific pair of hypotheses and a proposed sequence of experiments, we don’t need a general Convergence Theorem to tell us the likelihood of obtaining refuting evidence. The specific hypotheses $h_i$ and $h_j$ tell us this themselves . They tell us the likelihood of obtaining each specific outcome stream, including those that either refute the competitor or produce a very small likelihood ratio for it. Furthermore, after we’ve actually performed an experiment and recorded its outcome, all that matters is the actual ratio of likelihoods for that outcome. Convergence theorems become moot.

The point of the Likelihood Ratio Convergence Theorem (both the Falsification Theorem and the part of the theorem still to come) is to assure us in advance of considering any specific pair of hypotheses that if the possible evidence streams that test hypotheses have certain characteristics which reflect the empirical distinctness of the two hypotheses, then it is highly likely that one of the sequences of outcomes will occur that yields a very small likelihood ratio. These theorems provide finite lower bounds on how quickly such convergence is likely to be. Thus, they show that the CoA is satisfied in advance of our using the logic to test specific pairs of hypotheses against one another.

The Falsification Theorem applies whenever the evidence stream includes possible outcomes that may falsify the alternative hypothesis. However, it completely ignores the influence of any experiments or observations in the evidence stream on which hypothesis $h_j$ is fully outcome-compatible with hypothesis $h_i$. We now turn to a theorem that applies to those evidence streams (or to parts of evidence streams) consisting only of experiments and observations on which hypothesis $h_j$ is fully outcome-compatible with hypothesis $h_i$. Evidence streams of this kind contain no possibly falsifying outcomes. In such cases the only outcomes of an experiment or observation $c_k$ for which hypothesis $h_j$ may specify 0 likelihoods are those for which hypothesis $h_i$ specifies 0 likelihoods as well.

Hypotheses whose connection with the evidence is entirely statistical in nature will usually be fully outcome-compatible on the entire evidence stream. So, evidence streams of this kind are undoubtedly much more common in practice than those containing possibly falsifying outcomes. Furthermore, whenever an entire stream of evidence contains some mixture of experiments and observations on which the hypotheses are not fully outcome compatible along with others on which they are fully outcome compatible , we may treat the experiments and observations for which full outcome compatibility holds as a separate subsequence of the entire evidence stream, to see the likely impact of that part of the evidence in producing values for likelihood ratios.

To cover evidence streams (or subsequences of evidence streams) consisting entirely of experiments or observations on which $h_j$ is fully outcome-compatible with hypothesis $h_i$ we will first need to identify a useful way to measure the degree to which hypotheses are empirically distinct from one another on such evidence. Consider some particular sequence of outcomes $e^n$ that results from observations $c^n$. The likelihood ratio $P[e^n \pmid h_{j}\cdot b\cdot c^{n}] / P[e^n \pmid h_{i}\cdot b\cdot c^{n}]$ itself measures the extent to which the outcome sequence distinguishes between $h_i$ and $h_j$. But as a measure of the power of evidence to distinguish among hypotheses, raw likelihood ratios provide a rather lopsided scale, a scale that ranges from 0 to infinity with the midpoint, where $e^n$ doesn’t distinguish at all between $h_i$ and $h_j$, at 1. So, rather than using raw likelihood ratios to measure the ability of $e^n$ to distinguish between hypotheses, it proves more useful to employ a symmetric measure. The logarithm of the likelihood ratio provides such a measure.

Definition: QI—the Quality of the Information . For each experiment or observation $c_k$, define the quality of the information provided by possible outcome $o_{ku}$ for distinguishing $h_j$ from $h_i$, given b , as follows (where henceforth we take “logs” to be base-2):

Similarly, for the sequence of experiments or observations $c^n$, define the quality of the information provided by possible outcome $e^n$ for distinguishing $h_j$ from $h_i$, given b , as follows:

That is, QI is the base-2 logarithm of the likelihood ratio for $h_i$ over that for $h_j$.

So, we’ll measure the Quality of the Information an outcome would yield in distinguishing between two hypotheses as the base-2 logarithm of the likelihood ratio. This is clearly a symmetric measure of the outcome’s evidential strength at distinguishing between the two hypotheses. On this measure hypotheses $h_i$ and $h_j$ assign the same likelihood value to a given outcome $o_{ku}$ just when $\QI[o_{ku} \pmid h_i /h_j \pmid b\cdot c_k] = 0$. Thus, QI measures information on a logarithmic scale that is symmetric about the natural no-information midpoint, 0. This measure is set up so that positive information favors $h_i$ over $h_j$, and negative information favors $h_j$ over $h_i$.

Given the Independent Evidence Assumptions with respect to each hypothesis, it’s easy to show that the QI for a sequence of outcomes is just the sum of the QIs of the individual outcomes in the sequence:

Probability theorists measure the expected value of a quantity by first multiplying each of its possible values by their probabilities of occurring, and then summing these products. Thus, the expected value of QI is given by the following formula:

Definition: EQI—the Expected Quality of the Information . We adopt the convention that if $P[o_{ku} \pmid h_{i}\cdot b\cdot c_{k}] = 0$, then the term $\QI[o_{ku} \pmid h_i /h_j \pmid b\cdot c_k] \times P[o_{ku} \pmid h_{i}\cdot b\cdot c_{k}] = 0$. This convention will make good sense in the context of the following definition because, whenever the outcome $o_{ku}$ has 0 probability of occurring according to $h_i$ (together with $b\cdot c_k)$, it makes good sense to give it 0 impact on the ability of the evidence to distinguish between $h_j$ and $h_i$ when $h_i$ (together with $b\cdot c_k)$ is true. Also notice that the full outcome-compatibility of $h_j$ with $h_i$ on $c_k$ means that whenever $P[e_k \pmid h_{j}\cdot b\cdot c_{k}] = 0$, we must have $P[e_k \pmid h_{i}\cdot b\cdot c_{k}] = 0$ as well; so whenever the denominator would be 0 in the term

the convention just described makes the term

Thus the following notion is well-defined:

For $h_j$ fully outcome-compatible with $h_i$ on experiment or observation $c_k$, define

Also, for $h_j$ fully outcome-compatible with $h_i$ on each experiment and observation in the sequence $c^n$, define

The EQI of an experiment or observation is the Expected Quality of its Information for distinguishing $h_i$ from $h_j$ when $h_i$ is true. It is a measure of the expected evidential strength of the possible outcomes of an experiment or observation at distinguishing between the hypotheses when $h_i$ (together with $b\cdot c)$ is true. Whereas QI measures the ability of each particular outcome or sequence of outcomes to empirically distinguish hypotheses, EQI measures the tendency of experiments or observations to produce distinguishing outcomes. It can be shown that EQI tracks empirical distinctness in a very precise way. We return to this in a moment.

It is easily seen that the EQI for a sequence of observations $c^n$ is just the sum of the EQIs of the individual observations $c_k$ in the sequence:

(For proof see the supplement Proof that the EQI for $c^n$ is the sum of the EQI for the individual $c_k$ .)

This suggests that it may be useful to average the values of the $\EQI[c_k \pmid h_i /h_j \pmid b]$ over the number of observations n to obtain a measure of the average expected quality of the information among the experiments and observations that make up the evidence stream $c^n$.

Definition: The Average Expected Quality of Information For $h_j$ fully outcome-compatible with $h_i$ on each experiment and observation in the evidence stream $c^n$, define the average expected quality of information, $\bEQI$, from $c^n$ for distinguishing $h_j$ from $h_i$, given $h_i\cdot b$, as follows:

It turns out that the value of $\EQI[c_k \pmid h_i /h_j \pmid b_{}]$ cannot be less than 0; and it must be greater than 0 just in case $h_i$ is empirically distinct from $h_j$ on at least one outcome $o_{ku}$—i.e., just in case it is empirically distinct in the sense that $P[o_{ku} \pmid h_{i}\cdot b\cdot c_{k}] \ne P[o_{ku} \pmid h_{j}\cdot b\cdot c_{k}]$, for at least one outcome $o_{ku}$. The same goes for the average, $\bEQI[c^n \pmid h_i /h_j \pmid b]$.

Theorem: Nonnegativity of EQI.

$\EQI[c_k \pmid h_i /h_j \pmid b_{}] \ge 0$; and $\EQI[c_k \pmid h_i /h_j \pmid b_{}] \gt 0$ if and only if for at least one of its possible outcomes $o_{ku}$,

As a result, $\bEQI[c^n \pmid h_i /h_j \pmid b] \ge 0$; and $\bEQI[c^n \pmid h_i /h_j \pmid b] \gt 0$ if and only if at least one experiment or observation $c_k$ has at least one possible outcome $o_{ku}$ such that

(For proof, see the supplement The Effect on EQI of Partitioning the Outcome Space More Finely—Including Proof of the Nonnegativity of EQI .)

In fact, the more finely one partitions the outcome space $O_{k} = \{o_{k1},\ldots ,o_{kv},\ldots ,o_{kw}\}$ into distinct outcomes that differ on likelihood ratio values, the larger EQI becomes. [ 15 ] This shows that EQI tracks empirical distinctness in a precise way. The importance of the Non-negativity of EQI result for the Likelihood Ratio Convergence Theorem will become clear in a moment.

We are now in a position to state the second part of the Likelihood Ratio Convergence Theorem . It applies to all evidence streams not containing possibly falsifying outcomes for $h_j$ when $h_i$ holds—i.e., it applies to all evidence streams for which $h_j$ is fully outcome-compatible with $h_i$ on each $c_k$ in the stream.

Likelihood Ratio Convergence Theorem 2—The Probabilistic Refutation Theorem.

Suppose the evidence stream $c^n$ contains only experiments or observations on which $h_j$ is fully outcome-compatible with $h_i$—i.e., suppose that for each condition $c_k$ in sequence $c^n$, for each of its possible outcomes possible outcomes $o_{ku}$, either $P[o_{ku} \pmid h_{i}\cdot b\cdot c_{k}] = 0$ or $P[o_{ku} \pmid h_{j}\cdot b\cdot c_{k}] \gt 0$. In addition (as a slight strengthening of the previous supposition), for some $\gamma \gt 0$ a number smaller than $1/e^2$ ($\approx .135$; where e ’ is the base of the natural logarithm), suppose that for each possible outcome $o_{ku}$ of each observation condition $c_k$ in $c^n$, either $P[o_{ku} \pmid h_{i}\cdot b\cdot c_{k}] = 0$ or

And suppose that the Independent Evidence Conditions hold for evidence stream $c^n$ with respect to each of these hypotheses. Now, choose any positive $\varepsilon \lt 1$, as small as you like, but large enough (for the number of observations n being contemplated) that the value of

For $\varepsilon = 1/2^m$ and $\gamma = 1/2^q$, this formula becomes,

(For proof see the supplement Proof of the Probabilistic Refutation Theorem .)

This theorem provides sufficient conditions for the likely refutation of false alternatives via exceeding small likelihood ratios. The conditions under which this happens characterize the degree to which the hypotheses involved are empirically distinct from one another. The theorem says that when these conditions are met, according to hypothesis $h_i$ (taken together with $b\cdot c^n)$, the likelihood is near 1 that that one of the outcome sequence $e^n$ will occur for which the likelihood ratio is smaller than $\varepsilon$ (for any value of $\varepsilon$ you may choose). The likelihood of getting such an evidential outcome $e^n$ is quite close to 1—i.e., no more than the amount

below 1. (Notice that this amount below 1 goes to 0 as n increases.)

It turns out that in almost every case (for almost any pair of hypotheses) the actual likelihood of obtaining such evidence (i.e., evidence that has a likelihood ratio value less than $\varepsilon)$ will be much closer to 1 than this factor indicates. [ 16 ] Thus, the theorem provides an overly cautious lower bound on the likelihood of obtaining small likelihood ratios. It shows that the larger the value of $\bEQI$ for an evidence stream, the more likely that stream is to produce a sequence of outcomes that yield a very small likelihood ratio value. But even if $\bEQI$ remains quite small, a long enough evidence stream, n , of such low-grade evidence will, nevertheless, almost surely produce an outcome sequence having a very small likelihood ratio value. [ 17 ]

Notice that the antecedent condition of the theorem, that “either

for some $\gamma \gt 0$ but less than $1/e^2$ ($\approx .135$)”, does not favor hypothesis $h_i$ over $h_j$ in any way. The condition only rules out the possibility that some outcomes might furnish extremely strong evidence against $h_j$ relative to $h_i$—by making $P[o_{ku} \pmid h_{i}\cdot b\cdot c_{k}] = 0$ or by making

less than some quite small $\gamma$. This condition is only needed because our measure of evidential distinguishability, QI, blows up when the ratio

is extremely small. Furthermore, this condition is really no restriction at all on possible experiments or observations. If $c_k$ has some possible outcome sentence $o_{ku}$ that would make

(for a given small $\gamma$ of interest), one may disjunctively lump $o_{ku}$ together with some other outcome sentence $o_{kv}$ for $c_k$. Then, the antecedent condition of the theorem will be satisfied, but with the sentence ‘$(o_{ku} \vee o_{kv})$’ treated as a single outcome. It can be proved that the only effect of such “disjunctive lumping” is to make $\bEQI$ smaller than it would otherwise be (whereas larger values of $\bEQI$ are more desirable). If the too strongly refuting disjunct $o_{ku}$ actually occurs when the experiment or observation $c_k$ is conducted, all the better, since this results in a likelihood ratio

smaller than $\gamma$ on that particular evidential outcome. We merely failed to take this more strongly refuting possibility into account when computing our lower bound on the likelihood that refutation via likelihood ratios would occur.

The point of the two Convergence Theorems explored in this section is to assure us, in advance of the consideration of any specific pair of hypotheses, that if the possible evidence streams that test them have certain characteristics which reflect their evidential distinguishability, it is highly likely that outcomes yielding small likelihood ratios will result. These theorems provide finite lower bounds on how quickly convergence is likely to occur. Thus, there is no need to wait through some infinitely long run for convergence to occur. Indeed, for any evidence sequence on which the probability distributions are at all well behaved, the actual likelihood of obtaining outcomes that yield small likelihood ratio values will inevitably be much higher than the lower bounds given by Theorems 1 and 2.

In sum, according to Theorems 1 and 2, each hypothesis $h_i$ says , via likelihoods, that given enough observations, it is very likely to dominate its empirically distinct rivals in a contest of likelihood ratios. The true hypothesis speaks truthfully about this, and its competitors lie. Even a sequence of observations with an extremely low average expected quality of information is very likely to do the job if that evidential sequence is long enough. Thus (by Equation 9* ), as evidence accumulates, the degree of support for false hypotheses will very probably approach 0, indicating that they are probably false; and as this happens, (by Equations 10 and 11) the degree of support for the true hypothesis will approach 1, indicating its probable truth. Thus, the Criterion of Adequacy (CoA) is satisfied.

Up to this point we have been supposing that likelihoods possess objective or agreed numerical values. Although this supposition is often satisfied in scientific contexts, there are important settings where it is unrealistic, where hypotheses only support vague likelihood values, and where there is enough ambiguity in what hypotheses say about evidential claims that the scientific community cannot agree on precise values for the likelihoods of evidential claims. [ 18 ] Let us now see how the supposition of precise, agreed likelihood values may be relaxed in a reasonable way.

Recall why agreement, or near agreement, on precise values for likelihoods is so important to the scientific enterprise. To the extent that members of a scientific community disagree on the likelihoods, they disagree about the empirical content of their hypotheses, about what each hypothesis says about how the world is likely to be. This can lead to disagreement about which hypotheses are refuted or supported by a given body of evidence. Similarly, to the extent that the values of likelihoods are only vaguely implied by hypotheses as understood by an individual agent, that agent may be unable to determine which of several hypotheses is refuted or supported by a given body of evidence.

We have seen, however, that the individual values of likelihoods are not really crucial to the way evidence impacts hypotheses. Rather, as Equations 9–11 show, it is ratios of likelihoods that do the heavy lifting. So, even if two support functions $P_{\alpha}$ and $P_{\beta}$ disagree on the values of individual likelihoods, they may, nevertheless, largely agree on the refutation or support that accrues to various rival hypotheses, provided that the following condition is satisfied:

whenever possible outcome sequence $e^n$ makes \[\frac{P_{\alpha}[e^n \pmid h_{j}\cdot b\cdot c^{n}]}{P_{\alpha}[e^n \pmid h_{i}\cdot b\cdot c^{n}]} \lt 1,\] it also makes \[\frac{P_{\beta}[e^n \pmid h_{j}\cdot b\cdot c^{n}]}{P_{\beta}[e^n \pmid h_{i}\cdot b\cdot c^{n}]} \lt 1;\]
whenever possible outcome sequence $e^n$ makes \[\frac{P_{\alpha}[e^n \pmid h_{j}\cdot b\cdot c^{n}]}{P_{\alpha}[e^n \pmid h_{i}\cdot b\cdot c^{n}]} \gt 1,\] it also makes \[\frac{P_{\beta}[e^n \pmid h_{j}\cdot b\cdot c^{n}]}{P_{\beta}[e^n \pmid h_{i}\cdot b\cdot c^{n}]} \gt 1;\]
each of these likelihood ratios is either close to 1 for both of these support functions, or is quite far from 1 for both of them. [ 19 ]

When this condition holds, the evidence will support $h_i$ over $h_j$ according to $P_{\alpha}$ just in case it does so for $P_{\beta}$ as well, although the strength of support may differ. Furthermore, although the rate at which the likelihood ratios increase or decrease on a stream of evidence may differ for the two support functions, the impact of the cumulative evidence should ultimately affect their refutation or support in much the same way.

When likelihoods are vague or diverse, we may take an approach similar to that we employed for vague and diverse prior plausibility assessments. We may extend the vagueness sets for individual agents to include a collection of inductive support functions that cover the range of values for likelihood ratios of evidence claims (as well as cover the ranges of comparative support strengths for hypotheses due to plausibility arguments within b , as represented by ratios of prior probabilities). Similarly, we may extend the diversity sets for communities of agents to include support functions that cover the ranges of likelihood ratio values that arise within the vagueness sets of members of the scientific community.

This broadening of vagueness and diversity sets to accommodate vague and diverse likelihood values makes no trouble for the convergence to truth results for hypotheses. For, provided that the Directional Agreement Condition is satisfied by all support functions in an extended vagueness or diversity set under consideration, the Likelihood Ratio Convergence Theorem applies to each individual support function in that set. For, the proof of that convergence theorem doesn’t depend on the supposition that likelihoods are objective or have intersubjectively agreed values. Rather, it applies to each individual support function $P_{\alpha}$. The only possible problem with applying this result across a range of support functions is that when their values for likelihoods differ, function $P_{\alpha}$ may disagree with $P_{\beta}$ on which of the hypotheses is favored by a given sequence of evidence. That can happen because different support functions may represent the evidential import of hypotheses differently, by specifying different likelihood values for the very same evidence claims. So, an evidence stream that favors $h_i$ according to $P_{\alpha}$ may instead favor $h_j$ according to $P_{\beta}$. However, when the Directional Agreement Condition holds for a given collection of support functions, this problem cannot arise. Directional Agreement means that the evidential import of hypotheses is similar enough for $P_{\alpha}$ and $P_{\beta}$ that a sequence of outcomes may favor a hypothesis according to $P_{\alpha}$ only if it does so for $P_{\beta}$ as well.

Thus, when the Directional Agreement Condition holds for all support functions in a vagueness or diversity set that is extended to include vague or diverse likelihoods, and provided that enough evidentially distinguishing experiments or observations can be performed, all support functions in the extended vagueness or diversity set will very probably come to agree that the likelihood ratios for empirically distinct false competitors of a true hypothesis are extremely small. As that happens, the community comes to agree on the refutation of these competitors, and the true hypothesis rises to the top of the heap. [ 20 ]

What if the true hypothesis has evidentially equivalent rivals? Their posterior probabilities must rise as well. In that case we are only assured that the disjunction of the true hypothesis with its evidentially equivalent rivals will be driven to 1 as evidence lays low its evidentially distinct rivals. The true hypothesis will itself approach 1 only if either it has no evidentially equivalent rivals, or whatever equivalent rivals it does have can be laid low by plausibility arguments of a kind that don’t depend on the evidential likelihoods, but only show up via the comparative plausibility assessments represented by ratios of prior probabilities.

Enumerative Inductions: Bayesian Estimation and Convergence
Some Prominent Approaches to the Representation of Uncertain Inference
Likelihood Ratios, Likelihoodism, and the Law of Likelihood
Immediate Consequences of the Independent Evidence Conditions
Proof of the Falsification Theorem
Proof that the EQI for $c^n$ is the sum of EQI for the individual $c_k$
The Effect on EQI of Partitioning the Outcome Space More Finely—Including Proof of the Nonnegativity of EQI
Proof of the Probabilistic Refutation Theorem
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How to cite this entry . Preview the PDF version of this entry at the Friends of the SEP Society . Look up topics and thinkers related to this entry at the Internet Philosophy Ontology Project (InPhO). Enhanced bibliography for this entry at PhilPapers , with links to its database.

Confirmation and Induction . Really nice overview by Franz Huber in the Internet Encyclopedia of Philosophy .
Inductive Logic , (in PDF), by Branden Fitelson, Philosophy of Science: An Encyclopedia , (J. Pfeifer and S. Sarkar, eds.), Routledge. An extensive encyclopedia article on inductive logic.
Teaching Theory of Knowledge: Probability and Induction . A very extensive outline of issues in Probability and Induction, each topic accompanied by a list of relevant books and articles (without links), compiled by Brad Armendt and Martin Curd.
Probabilistic Confirmation Theory and Bayesian Reasoning . An annotated bibliography of influential works compiled by Timothy McGrew.
Bayesian Networks Without Tears , (in PDF), by Eugene Charniak (Computer Science and Cognitive Science, Brown University). An introductory article on Bayesian inference.
Miscellany of Works on Probabilistic Thinking . A collection of on-line articles on Subjective Probability and probabilistic reasoning by Richard Jeffrey and by several other philosophers writing on related issues.
Fitelson’s course on Confirmation Theory . Main page of Branden Fitelson’s course on Confirmation Theory. The Syllabus provides an extensive list of links to readings. The Notes, Handouts, & Links page has Fitelson’s weekly course notes and some links to useful internet resources on confirmation theory.
Fitelson’s course on Probability and Induction . Main page of Branden Fitelson’s course on Probability and Induction. The Syllabus provides an extensive list of links to readings on the subject. The Notes & Handouts page has Fitelson’s powerpoint slides for each of his lectures and some links to handouts for the course. The Links page contains links to some useful internet resources.

Bayes’ Theorem | epistemology: Bayesian | probability, interpretations of

Acknowledgments

Thanks to Alan Hájek, Jim Joyce, and Edward Zalta for many valuable comments and suggestions. The editors and author also thank Greg Stokley and Philippe van Basshuysen for carefully reading an earlier version of the entry and identifying a number of typographical errors.

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Deciphering Inductive Reasoning: Identifying Logic’s Gems

This essay about the concept of inductive reasoning, exploring its significance and application in various scenarios. It illustrates how inductive reasoning operates through specific examples, such as identifying patterns from observations, making predictive inferences, and drawing conclusions from empirical data. The essay emphasizes the nuanced nature of inductive reasoning, highlighting its role in guiding decision-making, scientific inquiry, and everyday life. Through these examples, it underscores the importance of recognizing and appreciating the subtle logic inherent in our observations and experiences. Additionally, PapersOwl presents more free essays samples linked to Reasoning.

How it works

In the vast landscape of reasoning, inductive reasoning stands tall, offering a method that’s both intricate and illuminating. It’s like being a detective, carefully piecing together clues to uncover truths hidden in the shadows. So, let’s embark on a quest to identify a passage that captures the essence of this subtle art.

Imagine this scenario: “Every time I munch on peanuts, my body throws a fit with allergic reactions. So, it’s safe to say I’m probably allergic to peanuts.

” Here, we witness the magic of inductive reasoning. Instead of relying on hard and fast rules or deductive leaps, the person gathers evidence from their own experiences. With each allergic reaction, they inch closer to the conclusion that peanuts trigger their allergies. It’s the accumulation of these experiences that builds the case, showcasing the power of inductive reasoning.

Now, let’s turn our attention to another example: “For the past month, the sun has faithfully risen in the east every single day. So, I’m betting it’ll do the same tomorrow.” Here, we see inductive reasoning in action yet again. By observing a consistent pattern over time—sunrise in the east—the person predicts the same outcome for the following day. It’s a simple yet profound demonstration of how past observations can guide future expectations, highlighting the beauty of inductive reasoning.

And how about this one: “After a month of mindfulness meditation, 90 out of 100 participants reported feeling happier. So, it’s likely that mindfulness meditation boosts happiness.” In this passage, we witness the marriage of inductive reasoning and empirical research. By studying a sample group and noting a consistent outcome—increased happiness—the researchers draw a broader conclusion about the effects of mindfulness meditation. It’s a testament to how inductive reasoning fuels the engine of scientific inquiry, pushing the boundaries of our understanding.

The goal of inductive reasoning is to skillfully navigate the muddy waters of uncertainty rather than striving for clarity. It’s about putting the pieces together, speculating sensibly, and appreciating the beauty of likelihood. We might admire the elegance of inductive reasoning as it permeates all aspect of our existence, from solving the secrets of nature to directing our daily decisions. Take a moment to observe the complex dance of logic that is being revealed to you the next time you come across a text that supports this line of thinking.

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Philosophy 110

Inductive Reasoning and Inductive Arguments

Deductive arguments, inductive arguments, deductive argument examples, inductive argument examples.

First in this tutorial/summary, let's recall the hat problem from Chapter 1 (also see the tutorial). In that story we noted that the prisoners were not going to be allowed to use inductive reasoning. Each prisoner had to be sure about what hat he had on his head, otherwise he would answer "no" (he did not know).

For instance, we noted one inductive possibility (probability only) for the first man (x):

If only three hats are used (one on x, one on y, and one on z), then we know x has a higher probability of having on a red hat IF he sees two white hats, one on y and one on z. If he sees two whites, then there is only one chance for him to have on a white hat but two chances for him to have on a red hat.

Consider the take away message from this example.

We can be smart using inductive reasoning even though we do not have certainty for our inference. We can also be dumb and have less probability for our inference . If x was allowed to choose based on probability, he would be smart to choose red and not very smart choosing white. Choosing red is wiser because the probability is higher - there are two red hats left but only one white. So, x would not be certain that he had on a red hat but he would be smart (playing the odds) if he chose red as the most probable.
We use inductive reasoning every day and should think hard about how to have less risk, more probability, and stronger inductive inferences . We make decisions that involve risk. We cannot be certain of what the future will bring. Something that worked in the past may not work in the future. We want to make decisions backed by evidence that lesson risk and increase the probability of success. We want what we will call strong inductive arguments . We want to avoid weak inductive arguments .
Very strong inductive conclusions = reliable beliefs . (See the optional tutorial for C2.) Reliable beliefs are beliefs backed by lots of evidence, even though we cannot be absolutely certain that they are true. We have overwhelming inductive evidence that people will fall off tall buildings if they jump due to gravity, even though we have not and cannot test every human being. Based on the evidence, we have a reliable and practical belief even though we believe in a big generalization.

Categorizing inductive arguments as strong v weak is similar to categorizing arguments as valid or invalid for deductive arguments. But there will not be a crisp cut off between strong v weak arguments. See the barrel full of apples example in the textbook (C3). The point of this example is that there is a sliding scale from weak to strong inductive inferences, but never certainty for any inductive inference, no matter how strong the evidence is for the inference.

Most of our future discussion on inductive reasoning will be on how to make inductive arguments stronger, and avoid weak inductive arguments such as those discussed in Chapter 5. But let's do the basics first and get some practice just seeing the difference between deductive and inductive reasoning.

(Valid or Invalid)

Valid arguments succeed in achieving this goal IF the premises are true (sound argument). Invalid arguments fail in achieving this goal EVEN IF the premises are true.

Arguments where the goal (to achieve strong and reliable beliefs) is to provide the best available evidence for the conclusion; the nature of the inferential claim is such that it is unlikely that the premises are true and the conclusion false .

Strong inductive arguments achieve this goal - providing the best available evidence. Weak inductive arguments do not.

Here are some examples:

All Internet hackers and spies for the Chinese government are Chinese. Wen Ho Lee is Chinese. So, Wen Ho Lee is an Internet hacker and spy for the Chinese government.

All Chinese people are Internet hackers and spies for the Chinese government.. Wen Ho Lee is Chinese. So, Wen Ho Lee is an Internet hacker and spy for the Chinese government.

Take Away Point: Both arguments are attempting to provide conclusive evidence for the conclusion. They are attempting to deduce a conclusion from a general statement and information about Wen Ho Lee. These arguments are not using any language that would indicate that the conclusions are only probably true. They are both implying a slam dunk conclusion. The first one though fails in this attempt; it is invalid. The second one partially accomplished the goal of conclusive evidence for the conclusion; it is valid. But the premises would have to all be true for the conclusion to be conclusive.

After careful observation we have not seen any hummingbirds all day in this forest. Therefore, probably there are no any hummingbirds in this forest.

After careful observation by trained hummingbird specialists over many weeks, no hummingbirds or signs of hummingbird habitation were found in this forest. Therefore, probably there are no hummingbirds in this forest.

15 Inductive Reasoning Examples

inductive reasoning example and definition, explained below

Inductive reasoning involves using patterns from small datasets to come up with broader generalizations. For example, it is used in opinion polling when you poll 1,000 people and use that data to come up with an estimate of broader public opinion.

Typically, inductive reasoning moves from the specific to the general; and can be understood as educated guesses, assumptions and/or hypotheses drawn from specific incidents.

However, it also has its weaknesses. It cannot provide concrete evidence because it always relies extrapolation and probability.

Inductive logic or inductive reasoning is often contrasted with deductive reasoning which is where the general moves to the specific (in other words: what is generally assumed to be true as a broader phenomenon is assumed to hold in a specific case or circumstance).

Advantages and Disadvantages of Inductive Reasoning

Well-formulated inductive reasoning examples, 1. polling and surveys.

“We surveyed 1,000 people across the county and 520 of them said they will vote to re-elect the mayor. We estimate that 52% of the county will vote for the mayor and he will be re-elected.”

Many statisticians make a living from conducting tried-and-true inductive reasoning studies. We often call this “polling data”. Polls will look at a sample size that is often large enough to have a 95% probability of being correct (that is p = <0.05 ) which is the generally accepted threshold of probability in academic studies.

Polls can help governments and politicians to create policies that are responsive to popular opinion.

However, polls are not always right, and often, statisticians have to re-calibrate their metrics after every general election to get a better understanding of polling bias.

For example, if the statisticians conduct their polls by phone, it may be the case that older people tend to answer their phone more than younger people, and older people may skew their vote in one way or another, which skews the overall polling numbers! They need to account for these biases, which makes their job of making generalizations from patterns very difficult at times.

2. Bonus Structure

“In a study of fifteen employees in my business, I found that a 10% bonus structure raised revenues by 20%. I will now roll-out the bonus structure to all employees.”

In this example of reasoning , a business owner has used a small dataset to identify a trend, which gave them sufficient confidence to roll out their intervention across the entire workplace.

If the business owner didn’t do this initial study, they wouldn’t have any indicative data to rely upon in order to feel confident about their decision. Here, we see how inductive reasoning can be used to help us make more informed decisions.

This doesn’t mean that the business owner will have the same success rate when he introduces the bonuses to everyone, but at least he can proceed with greater confidence than before.

3. Seasonal Trends

“For five years in a row, I have seen bears in the woods in June but not May. This year, I expect to wait until June to see a bear in the woods.”

We can also use inductive reasoning to make assumptions in our own lives. In the above example, a person who lives near the woods has identified a seasonal trend that allows them to generalize and predict future patterns.

This sort of seasonal prediction has been around for millennia. Nomads saw patterns in the land and decided to go on annual migrations based on their hypotheses that certain lands would be more fertile at certain times of year. Similarly, agriculturalists use seasonal trends to reason about when to plant their seeds. This doesn’t mean every year will be perfect (to this day, some seasons are terrible for crop yield).

4. Archaeological Digs

“We dug up three pots within a thirty square foot area. We should focus our dig efforts on this area to see what else we can dig up.”

Archaeology also regularly relies upon inductive reasoning. An archaeologist will find signs of human occupation in a location and use those signs as reason the intensify focus on that area.

In these instances, they are inducing that there are likely to be more remnants of civilization around the first remnants due to the assumption that humans may have settled or camped in that specific location.

5. Traffic Patterns

“I have noticed that traffic is bad between 7.30am and 9am. I will drive to the grocery store after 9am to avoid the traffic.”

We even use inductive reason regularly when planning out our days. We make observations about the things around us and use them to make generalizations and predictions.

In the above example, the person has noticed that traffic is worst just before the work day begins, so avoids driving during that period. This is a generalization that can help the person make informed decisions. While it’s not guaranteed that traffic will be better at 9.30am than 8.30am (there may be a car crash at any time of day!), inductive reasoning states that it is likely that traffic will be better at 9.30am than 8.30am.

Poorly-Formulated Inductive Reasoning Examples

6. dog breeds.

“Despite what the government says about Pitt Bulls, the only Pitt Bulls I have ever met were extremely friendly and sweet. Pitt Bulls must therefore not be a dangerous breed.”

While it may well be the case that this person has not personally encountered a hostile or aggressive Pitt Bull, numerous studies have been done indicating that Pitt Bulls, on average, are more aggressive than other dog breeds; whether or not this is inherently true remains speculation. Many cities have also banned the breed since they’ve resulted in the vast majority of dog fatally-related incidents and injuries , relative to the other dog breeds that exist.

This example illustrates how inductive logic goes from specific incidences and applies them as a general rule or conclusion on a given matter.

7. Job Salary and Occupation

“John is a lawyer, and he makes a lot of money. All lawyers make tons of money.”

Appearances can be deceiving, and though basic logic might indicate that something is true, it does not always hold in each situation. While it’s reasonable to assume that people within a certain occupation may earn a lot of money since, generally speaking, the job is associated with a higher salary—it is not always the case in every circumstance.

Some lawyers, for example, do pro-bono work, others may be employed by the government and work as public defenders for individuals that may lack the means to hire their own legal counsel.

8. Nationality

“My dad is Russian and he has blonde hair and blue eyes. All Russian people must have blonde hair and blue eyes.”

This illustrates the inductive reasoning fallacy by moving from an isolated or single case and applying it as a general rule or broadly applicable conclusion. We know that just because a person bears certain physical traits that may be generally affiliated with a geographical region, that does not mean all individuals from the same place will share those same physical traits.

This shows how inductive reasoning can result in incorrect conclusions and/or false assumptions by using specific instances to draw conclusions.

9. Left-Handedness

“All of my siblings are left-handed, and we are all talented artists. People that are left-handed are more creative and artistically inclined than those that are right-handed.”

It could seem reasonable for this person to assume (based on the evidence that they are exposed to,) that left-handed people are naturally more creative and artistic than their right-handed counterparts. Despite appearances, it is not proven that left-handed people are in fact more artistic than right-handed people .

The misstep in logic occurs from making the move from the specific to the general without having sufficient evidence to substantiate the claim as a generally applicable rule.

10. Rainy Weather

“I was in Seattle for a week, and it rained for all seven days I was there. It is always raining in Seattle.”

There’s no question that Seattle gets a lot of rain and is objectively regarded as a very rainy city. Even still, it would be false to conclude that it rains every single day without fail since this is not the case.

To correct the false conclusion or error in logic, we would revise the statement to some form of the following—each day I was in Seattle it rained; therefore, it is often raining in Seattle.

11. Buying Avocados

“While shopping for groceries, I was in the produce section checking for ripe avocados. I picked up one avocado and it was not ripe enough to eat. I picked up another and it was also underripe. There must not be any ripe avocados at this grocery store.”

While it’s possible that there are not any ripe avocados at the grocery store the person is perusing, this is not conclusive until he or she has inspected each avocado in the bin on how its ripeness. It’s clear that picking up a few avocados and determining that they are not ripe enough to eat does not necessarily indicate the remaining avocados in the bin will be underripe. This abrogates logic and demonstrates the error in inductive reasoning.

12. Food Poisoning

“The last time I ate at this Japanese restaurant I got terrible food poisoning. Do not go and eat at this Japanese restaurant because you will get food poisoning and be extremely sick.”

One incident of food poisoning does not indicate a general pattern or broad truth, and it certainly does not follow that just because a person got food poisoning from eating at a restaurant one time, anyone who eats at that same restaurant will necessarily get food poisoning.

The problem with fallacies in inductive reasoning is that it looks to establish a claim on what is true and factual in general, and while it may well be true in an individual case, it is unlikely to hold in each case without fail.

13. Buying A Mattress

“I have purchased four different mattresses on Amazon. None of them were comfortable, and so I returned all four. Amazon doesn’t have good-quality mattresses.”

This takes a similar structure to the previous example on buying avocados. It’s clear how it would be tempting for this person to conclude, based on their personal experience, that Amazon doesn’t have decent mattresses available to purchase.

However, until the person has actually tried each mattress for sale on Amazon, they cannot say conclusively that all mattresses for sale on Amazon are of poor quality. This would be a false assumption that uses the fallacy of inductive reasoning to draw a conclusion.

14. Penguins

“Penguins are birds and they can’t fly. Therefore, it must be true that birds cannot fly.”

Penguins are a kind of bird and cannot fly; but this does not mean that birds, in general, cannot fly. We know birds can fly—so to assume that birds cannot fly because penguins cannot fly is false and uses flawed inductive logic to formulate its conclusion.

If a person saw a crow and said “crows are birds and can fly, so all birds can fly”, it would also be a false inductive generalization. The person should gather a larger dataset of different types of birds before formulating their hypothesis.

15. Rap Music

“The few rap songs that I’ve listened to included remarks that were inappropriate. Therefore, all rap music is inappropriate.”

While rap music can certainly have some uncouth lyrics, it is surely not the case that rap music is inherently bad, or that every single rap song that exists is not acceptable. There are many rap musicians who rap positive lyrics.

Therefore, this is an overgeneralization (often used by parents!) that aims to exclude the good with the bad, rather than taking a more nuanced look at the issue at hand.

The Role of Inductive Reasoning in Everyday Life

Categories: Critical Thinking Reasoning

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Published: Jan 29, 2024

Words: 889 | Pages: 2 | 5 min read

Definition and explanation of inductive reasoning, importance of inductive reasoning in everyday life, examples of inductive reasoning in different situations, challenges and limitations of inductive reasoning, strategies for enhancing inductive reasoning skills, everyday examples, professional examples.

Baron, J. (2008). Thinking and deciding. Cambridge University Press.
Halpern, D. F. (2014). Thought and knowledge: an introduction to critical thinking. Psychology Press.
Kennison, S. M. (2013). Introduction to critical thinking and creativity: think more, think better. Wiley.

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9.4: Analogical Reasoning

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It may be the most common type of inductive reasoning. It’s sort of built into our cognitive systems. What is it? Reasoning by analogy . We use the known or observed similarities of things to posit further similarities. In this edition of the textbook, we’ll cover this quite briefly. In future editions, I hope to expand this section to a more complete analysis.

The first thing to do is to look at some examples of analogical reasoning or reasoning by analogy:

Example $\PageIndex{1}$

If you increase the gauge of copper wire, you increase the maximum amperage that wire can conduct. Therefore, since water and electricity share many commonalities particularly with respect to their flow, we should see an increase in flow rate with an increase in pipe diameter.

Example $\PageIndex{2}$

Electrons and Photons are both point particles, therefore we should expect electrons to travel at or near the speed of light.

Example $\PageIndex{3}$

My boyfriend is acting just like that character on television that is cheating on their partner, so my boyfriend is probably cheating.

Example $\PageIndex{4}$

Samsung produces both the Galaxy S8 and the Galaxy S9, so I would expect their basic operating systems to be quite similar.

Example $\PageIndex{5}$

Northern California and Northwestern Italy have very similar climates, so probably olives and grapes will grow well in Northern California.

One can see how this isn’t perfect reasoning, right? There are going to be loads of counterexamples to each of these types of analogical reasoning. But some of these particular examples turn out to have true conclusions and they seem to have identified a commonality between the two analogues (the things being compared in the analogy) that in fact makes the conclusion true . For instance, it is the similar climates of Northern California and Tuscany that makes for quite similar growing conditions and therefore the success of quite similar crops. It is the properties shared by all point particles that leads them to travel at or near the speed of light (it’s not impossible I’m wrong about this, I’m no physicist). It is the common manufacturer that makes Samsung phones similar. In other words: the commonalities between things often do cause them to have further commonalities.

Arguments by analogy have a certain structure in common. It’s worth investigating this a bit further:

Let’s take an example from above and analyze it according to this schema:

Example $\PageIndex{6}$

Now we identify the analogues first:

It can be tricky to get the analogues just right, but the Attribute in Common can help us narrow it down.

Next we identify what is being inferred about the pair of analogues: what else are they supposed to have in common on the basis of their known similarities?

Finally, we might wonder what is it about the Attributes in Common that might lead us to believe that the analogues will share the further attribute(s)? What is the causal link? Here’s my best guess about what’s going on here (again, I’m no physicist):

This is a tricky example, since the right answer relies on physics I am not an expert in (not by a long stretch). What matters at this point is that we’re able to identify good and bad analogies by means of these tools of analysis. I won’t expect to find a link between attributes forthcoming in cases of bad analogical reasoning. Here’s an example:

Example $\PageIndex{7}$

Both of my pairs of shoes are blue, so I’d expect one [Stiletto heels] to perform just as well on a hike as the other [Chaco Sandals].

Let’s break down this analogy using our handy chart:

There is no link between color and function when it comes to shoes and so the analogy is a bad one. Even if the link is unknown or a bit mysterious (again, I’m no physicist, so the chart about electrons is probably a bit off), we can usually still identify something like the right link between attributes if we understand anything about how the analogy works. In the case of a bad analogy, we can make up wild stories, but at the end of the day we’ll most likely realize that there simply is no link between the attributes in question.

What about when the attribute in common isn’t connected to the attribute inferred? Well, it’s probably not a good bit of analogical reasoning, even if it ends up having a true conclusion. Here’s an example of this sort of thing:

Example $\PageIndex{8}$

Rolexes and Teslas are both expensive. Teslas are very durable, so it follows that Rolexes are very durable.

Let’s break it down:

Sure, they are both durable, but no, they are not both durable because they are expensive nor vice versa. They are expensive because of market forces like supply and demand, and they are durable because they are well-made. The two don’t really share much of a link, even if there is some sort of a weak link between the two.

In closing, analogical reasoning starts from some commonalities between two or more sets of things (analogues), and then it proceeds by inferring further commonalities. It is successful when either a) the common attributes cause or explain the inferred attributes; or b) the common and inferred attributes are both caused or explained by something else about the two analogues. It is unsuccessful when neither (a) nor (b) is true. Using the chart on offer in this section can be helpful in breaking down analogical reasoning and identifying just what is being claimed. Sometimes analyzing it like this can lay bare the faults in such forms of reasoning.

The following is a good discussion of the evaluation of analogical arguments from Matthew Knachels’ Fundamental Methods of Logic: (CC-BY-4.0 License):

The Evaluation of Analogical Arguments

Unlike in the case of deduction, we will not have to learn special techniques to use when evaluating these sorts of arguments. It’s something we already know how to do, something we typically do automatically and unreflectively. The purpose of this section, then, is not to learn a new skill, but rather subject a practice we already know how to engage in to critical scrutiny. We evaluate analogical arguments all the time without thinking about how we do it. We want to achieve a metacognitive perspective on the practice of evaluating arguments from analogy; we want to think about a type of thinking that we typically engage in without much conscious deliberation. We want to identify the criteria that we rely on to evaluate analogical reasoning—criteria that we apply without necessarily realizing that we’re applying them. Achieving such metacognitive awareness is useful insofar as it makes us more self-aware, critical, and therefore effective reasoners.

Analogical arguments are inductive arguments. They give us reasons that are supposed to make their conclusions more probable. How probable, exactly? That’s very hard to say. How probable was it that I would like The Wolf of Wall Street given that I had liked the other four Scorsese/DiCaprio collaborations? I don’t know. How probable is it that it’s wrong to eat pork given that it’s wrong to eat dogs and dolphins? I really don’t know. It’s hard to imagine how you would even begin to answer that question.

As we mentioned, while it’s often impossible to evaluate inductive arguments by giving a precise probability of its conclusion, it is possible to make relative judgments about strength and weakness. Recall, new information can change the probability of the conclusion of an inductive argument. We can make relative judgments of like this: if we add this new information as a premise, the new argument is stronger/weaker than the old argument; that is, the new information makes the conclusion more/less likely.

It is these types of relative judgments that we make when we evaluate analogical reasoning. We compare different arguments—with the difference being new information in the form of an added premise, or a different conclusion supported by the same premises—and judge one to be stronger or weaker than the other. Subjecting this practice to critical scrutiny, we can identify six criteria that we use to make such judgments.

We’re going to be making relative judgments, so we need a baseline argument against which to compare others. Here is such an argument:

Alice has taken four Philosophy courses during her time in college. She got an A in all four. She has signed up to take another Philosophy course this semester. I predict she will get an A in that course, too.

This is a simple argument from analogy, in which the future is predicted based on past experience. It fits the schema for analogical arguments: the new course she has signed up for is designated by ‘c’; the property we’re predicting it has (Q) is that it is a course Alice will get an A in; the analogues are the four previous courses she’s taken; what they have in common with the new course (P1) is that they are also Philosophy classes; and they all have the property Q—Sally got an A in each.

Anyway, how strong is the baseline argument? How probable is its conclusion in light of its premises? I have no idea. It doesn’t matter. We’re now going to consider tweaks to the argument, and the effect that those will have on the probability of the conclusion. That is, we’re going to consider slightly different arguments, with new information added to the original premises or changes to the prediction based on them, and ask whether these altered new arguments are stronger or weaker than the baseline argument. This will reveal the six criteria that we use to make such judgments. We’ll consider one criterion at a time.

Number of Analogues

Suppose we alter the original argument by changing the number of prior Philosophy courses Alice had taken. Instead of Alice having taken four philosophy courses before, we’ll now suppose she has taken 14. We’ll keep everything else about the argument the same: she got an A in all of them, and we’re predicting she’ll get an A in the new one. Are we more or less confident in the conclusion—the prediction of an A—with the altered premise? Is this new argument stronger or weaker than the baseline argument?

It’s stronger! We’ve got Alice getting an A 14 times in a row instead of only four. That clearly makes the conclusion more probable. (How much more? Again, it doesn’t matter.)

What we did in this case is add more analogues. This reveals a general rule: other things being equal, the more analogues in an analogical argument, the stronger the argument (and conversely, the fewer analogues, the weaker). The number of analogues is one of the criteria we use to evaluate arguments from analogy.

Variety of Analogues

You’ll notice that the original argument doesn’t give us much information about the four courses Alice succeeded in previously and the new course she’s about to take. All we know is that they’re all Philosophy courses. Suppose we tweak things. We’re still in the dark about the new course Alice is about to take, but we know a bit more about the other four: one was a course in Ancient Greek Philosophy; one was a course on Contemporary Ethical Theories; one was a course in Formal Logic; and the last one was a course in the Philosophy of Mind. Given this new information, are we more or less confident that she will succeed in the new course, whose topic is unknown to us? Is the argument stronger or weaker than the baseline argument?

It is stronger. We don’t know what kind of Philosophy course Alice is about to take, but this new information gives us an indication that it doesn’t really matter. She was able to succeed in a wide variety of courses, from Mind to Logic, from Ancient Greek to Contemporary Ethics. This is evidence that Alice is good at Philosophy generally, so that no matter what kind of course she’s about to take, she’ll probably do well in it.

Again, this points to a general principle about how we evaluate analogical arguments: other things being equal, the more variety there is among the analogues, the stronger the argument (and conversely, the less variety, the weaker).

Number of Similarities

In the baseline argument, the only thing the four previous courses and the new course have in common is that they’re Philosophy classes. Suppose we change that. Our newly tweaked argument predicts that Alice will get an A in the new course, which, like the four she succeeded in before, is cross-listed in the Department of Religious Studies and covers topics in the Philosophy of Religion. Given this new information—that the new course and the four older courses were similar in ways we weren’t aware of—are we more or less confident in the prediction that Alice will get another A? Is the argument stronger or weaker than the baseline argument?

Again, it is stronger. Unlike the last example, this tweak gives us new information both about the four previous courses and the new one. The upshot of that information is that they’re more similar than we knew; that is, they have more properties in common. To P1 = ‘is a Philosophy course’ we can add P2 = ‘is cross-listed with Religious Studies’ and P3 = ‘covers topics in Philosophy of Religion’. The more properties things have in common, the stronger the analogy between them. The stronger the analogy, the stronger the argument based on that analogy. We now know not just that Alice did well in not just in Philosophy classes—but specifically in classes covering the Philosophy of Religion; and we know that the new class she’s taking is also a Philosophy of Religion class. I’m much more confident predicting she’ll do well again than I was when all I knew was that all the classes were Philosophy; the new one could’ve been in a different topic that she wouldn’t have liked.

General principle: other things being equal, the more properties involved in the analogy—the more similarities between the item in the conclusion and the analogues—the stronger the argument (and conversely, the fewer properties, the weaker).

Number of Differences

An argument from analogy is built on the foundation of the similarities between the analogues and the item in the conclusion—the analogy. Anything that weakens that foundation weakens the argument. So, to the extent that there are differences among those items, the argument is weaker.

Suppose we add new information to our baseline argument: the four Philosophy courses Alice did well in before were all courses in the Philosophy of Mind; the new course is about the history of Ancient Greek Philosophy. Given this new information, are we more or less confident that she will succeed in the new course? Is the argument stronger or weaker than the baseline argument? Clearly, the argument is weaker. The new course is on a completely different topic than the other ones. She did well in four straight Philosophy of Mind courses, but Ancient Greek Philosophy is quite different. I’m less confident that she’ll get an A than I was before.

If I add more differences, the argument gets even weaker. Supposing the four Philosophy of Mind courses were all taught by the same professor (the person in the department whose expertise is in that area), but the Ancient Greek Philosophy course is taught by someone different (the department’s specialist in that topic). Different subject matter, different teachers: I’m even less optimistic about Alice’s continued success.

Generally speaking, other things being equal, the more differences there are between the analogues and the item in the conclusion, the weaker the argument from analogy.

Relevance of Similarities and Differences

Not all similarities and differences are capable of strengthening or weakening an argument from analogy, however. Suppose we tweak the original argument by adding the new information that the new course and the four previous courses all have their weekly meetings in the same campus building. This is an additional property that the courses have in common, which, as we just saw, other things being equal, should strengthen the argument. But other things are not equal in this case. That’s because it’s very hard to imagine how the location of the classroom would have anything to do with the prediction we’re making—that Alice will get an A in the course. Classroom location is simply not relevant to success in a course. 1 Therefore, this new information does not strengthen the argument. Nor does it weaken it; I’m not inclined to doubt Alice will do well in light of the information about location. It simply has no effect at all on my appraisal of her chances.

Similarly, if we tweak the original argument to add a difference between the new class and the other four, to the effect that while all of the four older classes were in the same building, while the new one is in a different building, there is no effect on our confidence in the conclusion. Again, the building in which a class meets is simply not relevant to how well someone does.

Contrast these cases with the new information that the new course and the previous four are all taught by the same professor. Now that strengthens the argument! Alice has gotten an A four times in a row from this professor—all the more reason to expect she’ll receive another one. This tidbit strengthens the argument because the new similarity—the same person teaches all the courses—is relevant to the prediction we’re making—that Alice will do well. Who teaches a class can make a difference to how students do—either because they’re easy graders, or because they’re great teachers, or because the student and the teacher are in tune with one another, etc. Even a difference between the analogues and the item in the conclusion, with the right kind of relevance, can strengthen an argument. Suppose the other four philosophy classes were taught be the same teacher, but the new one is taught by a TA—who just happens to be her boyfriend. That’s a difference, but one that makes the conclusion—that Alice will do well—more probable.

Generally speaking, careful attention must be paid to the relevance of any similarities and differences to the property in the conclusion; the effect on strength varies.

Modesty/Ambition of the Conclusion

Suppose we leave everything about the premises in the original baseline argument the same: four Philosophy classes, an A in each, new Philosophy class. Instead of adding to that part of the argument, we’ll tweak the conclusion. Instead of predicting that Alice will get an A in the class, we’ll predict that she’ll pass the course. Are we more or less confident that this prediction will come true? Is the new, tweaked argument stronger or weaker than the baseline argument?

It’s stronger. We are more confident in the prediction that Alice will pass than we are in the prediction that she will get another A, for the simple reason that it’s much easier to pass than it is to get an A. That is, the prediction of passing is a much more modest prediction than the prediction of an A.

Suppose we tweak the conclusion in the opposite direction—not more modest, but more ambitious. Alice has gotten an A in four straight Philosophy classes, she’s about to take another one, and I predict that she will do so well that her professor will suggest that she publish her term paper in one of the most prestigious philosophical journals and that she will be offered a three-year research fellowship at the Institute for Advanced Study at Princeton University. That’s a bold prediction! Meaning, of course, that it’s very unlikely to happen. Getting an A is one thing; getting an invitation to be a visiting scholar at one of the most prestigious academic institutions in the world is quite another. The argument with this ambitious conclusion is weaker than the baseline argument.

General principle: the more modest the argument’s conclusion, the stronger the argument; the more ambitious, the weaker.

Refutation by Analogy

We can use arguments from analogy for a specific logical task: refuting someone else’s argument, showing that it’s bad. Recall the case of deductive arguments. To refute those—to show that they are bad, i.e., invalid—we had to produce a counterexample—a new argument with the same logical form as the original that was obviously invalid, in that its premises were in fact true and its conclusion in fact false. We can use a similar procedure to refute inductive arguments. Of course, the standard of evaluation is different for induction: we don’t judge them according to the black and white standard of validity. And as a result, our judgments have less to do with form than with content. Nevertheless, refutation along similar lines is possible, and analogies are the key to the technique.

To refute an inductive argument, we produce a new argument that’s obviously bad—just as we did in the case of deduction. We don’t have a precise notion of logical form for inductive arguments, so we can’t demand that the refuting argument have the same form as the original; rather, we want the new argument to have an analogous form to the original. The stronger the analogy between the refuting and refuted arguments, the more decisive the refutation. We cannot produce the kind of knock-down refutations that were possible in the case of deductive arguments, where the standard of evaluation—validity—does not admit of degrees of goodness or badness, but the technique can be quite effective.

Consider the following:

“Duck Dynasty” star and Duck Commander CEO Willie Robertson said he supports Trump because both of them have been successful businessmen and stars of reality TV shows.

By that logic, does that mean Hugh Hefner’s success with “Playboy” and his occasional appearances on “Bad Girls Club” warrant him as a worthy president? Actually, I’d still be more likely to vote for Hefner than Trump. [2]

The author is refuting the argument of Willie Robertson, the “Duck Dynasty” star. Robertson’s argument is something like this: Trump is a successful businessman and reality TV star; therefore, he would be a good president. To refute this, the author produces an analogous argument—Hugh Hefner is a successful businessman and reality TV star; therefore, Hugh Hefner would make a good president—that he regards as obviously bad. What makes it obviously bad is that it has a conclusion that nobody would agree with: Hugh Hefner would make a good president. That’s how these refutations work. They attempt to demonstrate that the original argument is lousy by showing that you can use the same or very similar reasoning to arrive at an absurd conclusion.

Here’s another example, from a group called “Iowans for Public Education”. Next to a picture of an apparently well-to-do lady is the following text:

“My husband and I have decided the local parks just aren’t good enough for our kids. We’d rather use the country club, and we are hoping state tax dollars will pay for it. We are advocating for Park Savings Accounts, or PSAs. We promise to no longer use the local parks. To hell with anyone else or the community as a whole. We want our tax dollars to be used to make the best choice for our family.”

Sound ridiculous? Tell your legislator to vote NO on Education Savings Accounts (ESAs), aka school vouchers.

The argument that Iowans for Public Education put in the mouth of the lady on the poster is meant to refute reasoning used by advocates for “school choice”, who say that they ought to have the right to opt out of public education and keep the tax dollars they would otherwise pay for public schools and use it to pay to send their kids to private schools. A similar line of reasoning sounds pretty crazy when you replace public schools with public parks and private schools with country clubs.

Since these sorts of refutations rely on analogies, they are only as strong as the analogy between the refuting and refuted arguments. There is room for dispute on that question. Advocates for school vouchers might point out that schools and parks are completely different things, that schools are much more important to the future prospects of children, and that given the importance of education, families should have to right choose what they think is best. Or something like that. The point is, the kinds of knock-down refutations that were possible for deductive arguments are not possible for inductive arguments. There is always room for further debate.

[1] I’m sure someone could come up with some elaborate backstory for Alice according to which the location of the class somehow makes it more likely that she will do well, but set that aside. No such story is on the table here.

[2] Austin Faulds, “Weird celebrity endorsements fit for weird election,” Indiana Daily Student, 10/12/16, http://www.idsnews.com/article/2016/...weird-election .

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Using Inductive Reasoning

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How to Write a Deductive Essay: Step-by-Step

October 26, 2023

What is a Deductive Essay

A deductive essay, as the name suggests, is an essay type where the writer forms an argument by deductive reasoning. In other words, a writer makes a claim and presents evidence to support it. The goal of writing a deductive essay is to convince readers of the validity of an argument, thereby persuading them to accept the writer’s point of view.

The structure of a deductive essay is defined by the logical progression of the argument. The essay starts with a premise, which is a general statement that serves as a foundation for the argument. The premise is followed by supporting evidence, which can be in the form of observations or data, leading to a conclusion that is drawn from the evidence. Deductive reasoning closely resembles the scientific method of experiment and observation.

It is essential to be concise, clear, and logical when writing a deductive essay. A well-written deductive essay can persuade readers to accept an argument, while a poorly written one can leave readers confused. The following sections of this article will guide you through the various steps involved in writing a deductive essay and provide expert tips to help you successfully write a deductive essay.

Difference Between Deductive and Inductive Writing

While both deductive and inductive writing involve reasoning and making arguments, there are key differences between the two approaches. Understanding these differences is crucial when learning how to write a deductive essay.

Deductive writing starts with a general premise or statement and uses it to form a specific argument or conclusion. The writer presents evidence that supports the premise, leading to a logical deduction. Deductive reasoning is often used in scientific research, mathematics, and philosophy.

On the other hand, inductive writing begins with specific observations or evidence and then draws a general conclusion based on that evidence. Unlike deductive writing, inductive reasoning allows for a degree of uncertainty and generalization. This approach is common in fields such as social sciences, where researchers analyze data and patterns to formulate theories.

When writing a deductive essay, it is important to focus on deductive reasoning and provide clear evidence to support your argument. Avoid making broad generalizations or hasty conclusions based on limited evidence. By understanding the distinction between deductive and inductive writing, you can effectively structure and strengthen your deductive arguments in your essay.

How to Write a Deductive Essay: Step-by-Step Guide

1. choosing a topic.

Choosing a topic is a critical step when writing a deductive essay. A well-chosen topic sets the foundation for a strong and focused argument. When selecting a topic for your deductive essay, consider the following guidelines:

Relevance: Choose a topic that is relevant to your field of study or the subject you are writing about. Ensure that it is an area where deductive reasoning can be applied effectively.
Interesting and Engaging: Select a topic that captivates your interest and engages your readers. Choose something that you are passionate about, as it will make the writing process more enjoyable and yield better results.
Clarity and Scope: Opt for a topic that is clear and specific, allowing for a focused and concise essay. Avoid broad or ambiguous topics that may lead to a lack of clarity and difficulty in presenting a deductive argument.
Availability of Evidence: Ensure that there is sufficient evidence available to support your chosen topic. Conduct preliminary research to gauge the availability of relevant sources, data, or examples.
Originality: Consider choosing a unique or lesser-explored topic to stand out from the crowd. This will not only make your essay more interesting but also demonstrate your ability to think critically and creatively.

By carefully considering these factors, you can select a suitable topic that aligns with the requirements of a deductive essay and allows for a strong, focused, and compelling argument.

10 Deductive Essay Topics:

The effects of climate change on the world’s oceans.
The link between social media and anxiety disorders.
The impact of universal healthcare on the economy.
The relationship between violent video games and real-life aggression.
The effects of automation on employment in the manufacturing industry.
The importance of early childhood education in future academic success.
The role of genetics in the development of mental illnesses.
The effects of fast food consumption on obesity rates.
The significance of renewable energy in reducing carbon emissions.
The relationship between higher education and earning potential.

These topics cover a broad range of fields and issues while allowing for the application of deductive reasoning. When writing a deductive essay on any of these topics, ensure that the argument is structured logically, with evidence used effectively to support premises and conclusions.

2. Conducting Research

Conducting research is an essential step when writing a deductive essay. Research serves as the backbone of a deductive argument and provides evidence to support the premises and conclusion. Here are some tips to help you conduct effective research for your deductive essay:

Start with a clear understanding of the topic. This will help you to focus your research and find relevant sources of information.
Use credible sources such as peer-reviewed journals, trusted databases, and academic publications to ensure that your research is reliable and accurate.
Keep a detailed record of your sources to avoid plagiarism and allow for easy referencing.
Pay attention to the quality and quantity of your research. Ensure that you have enough sources to support your argument and that they are relevant to your topic.
Organize your research into relevant sections and group related information together. This will help you to identify patterns and connections that will strengthen your argument.
Analyze the data and information you have gathered, and use it to form your premises and conclusion. Ensure that all your evidence supports your argument and that there are no contradictions in your reasoning.

Conducting thorough research can make the difference between a convincing and well-supported deductive argument and one that falls short. By following these tips, you can conduct effective research and produce a successful deductive essay.

3. Developing a Thesis Statement

Developing a strong thesis statement is crucial when writing a deductive essay. The thesis statement provides the main argument or position that will be explored and supported throughout the essay. Here are some key steps to consider when developing your thesis statement:

Understand the assignment: Ensure you have a clear grasp of the essay prompt or assignment requirements. This will help you align your thesis statement with the expectations of the task.
Identify the main premise: Determine the primary premise or assumption on which your deductive argument will be based. This will serve as the foundation of your thesis statement.
Make it concise: Craft a clear and concise thesis statement that accurately conveys the main argument of your essay. Avoid vague or ambiguous wording that could weaken the impact of your statement.
Make it specific: Be specific in your thesis statement to guide your essay’s focus and ensure a well-defined argument. Use precise language and avoid broad generalizations.
Reflect the structure: Consider how your essay will be structured, such as outlining the major premises or evidence that will be presented. Your thesis statement should align with this structure and clearly indicate the direction your essay will take.

Remember that your thesis statement should be arguable and provable based on the deductive reasoning used in your essay. It should serve as a roadmap for both you as the writer and your reader, providing a clear and concise summary of the main argument you will be developing throughout your deductive essay.

4. Creating an Outline

Creating an outline is an essential step in the process of writing a deductive essay. It helps you organize your thoughts, structure your argument, and maintain a logical flow throughout your essay. Here are some key tips to consider when creating an outline for your deductive essay:

Introduction: Start with a captivating introduction that provides background information on your topic and presents your thesis statement. This section should grab the reader’s attention and set the stage for your deductive argument.
Premise 1: Introduce your first premise, providing a clear statement or fact that supports your thesis. Present evidence, examples, or quotes that reinforce this premise, showcasing its relevance and validity.
Premise 2: Introduce your second premise, which strengthens your argument and supports your thesis. Just like Premise 1, provide evidence and examples to substantiate this premise, making sure it logically connects to the previous premise.
Premise 3 (if applicable): If your deductive essay requires additional premises or evidence, include them in this section.
Conclusion: Summarize your premises and restate your thesis, drawing a logical conclusion based on the deductive reasoning presented. Emphasize the strength of your argument and its implications.
Counterarguments (optional): If relevant, address potential counterarguments and refute them using logical reasoning and evidence.

Remember, the outline serves as a roadmap for your essay, guiding your writing process and ensuring a well-structured and coherent argument. It allows you to organize your thoughts, identify any gaps in your reasoning, and maintain a logical flow throughout your deductive essay.

Sample Deductive Essay Outline:

I. Introduction A. Engaging hook to grab the reader’s attention B. Background information on the topic C. Clear thesis statement that conveys the main argument

II. Premise 1 A. Clear statement or fact that supports the thesis B. Explanation of how Premise 1 is relevant to the argument C. Presentation of evidence, examples, or quotes to support Premise 1

III. Premise 2 A. Introduction of the second premise that strengthens the argument B. Explanation of the logical connection between Premise 2 and Premise 1 C. Evidence, examples, or quotes to support Premise 2

IV. Premise 3 (if applicable) A. Optional section for including additional premises or evidence B. Clear statement or fact relevant to the argument C. Supporting evidence, examples, or quotes for Premise 3

V. Counterarguments (optional) A. Address potential counterarguments to the main argument B. Refute the counterarguments using logical reasoning and evidence

VI. Conclusion A. Summary of the premises presented in the essay B. Restatement of the thesis and its importance C. Drawing a logical conclusion based on the deductive reasoning presented

Remember, this is just a sample outline for a deductive essay and can be adapted to fit your specific topic and approach. The outline serves as a guide to help you structure your essay and maintain a logical flow of ideas.

5. Writing the Introduction for a Deductive Essay

The introduction of your deductive essay plays a crucial role in setting the tone and capturing the reader’s attention. It should provide the necessary context and background for the topic while clearly stating the purpose of your essay. Here are some key tips for writing an effective introduction for a deductive essay:

Start with a captivating hook: Begin your introduction with an engaging statement, question, or anecdote that grasps the reader’s interest. This will encourage them to continue reading and delve into your deductive argument.
Provide background information: Give a brief overview of the topic and any relevant historical, social, or cultural context. This will help orient the reader and demonstrate your knowledge and understanding of the subject matter.
Clearly state your purpose: In a concise and straightforward manner, clearly articulate that you are writing a deductive essay. This will set the expectations for the reader and inform them about the logical argument you will be presenting.
Present your thesis statement: Towards the end of the introduction, clearly state your thesis, which represents the main argument of your deductive essay. Make it explicit, concise, and specific to provide the reader with a clear roadmap of your essay.

Remember, the introduction should be engaging, informative, and concise. It should effectively introduce the topic, explain the purpose of your essay, and present your thesis statement, all while keeping the reader eager to explore your deductive argument further.

6. Presenting the Premises

Presenting the premises is a critical component of any deductive essay. A premise is a piece of evidence or statement that serves as the foundation for your argument and supports your thesis. When presenting your premises, you need to provide clear and convincing evidence, examples, or quotes to establish its relevance and validity. Here are some key tips to consider when presenting the premises of your deductive essay:

Make it clear: Ensure the premise is explicit and clearly stated, leaving no room for ambiguity or misinterpretation.
Provide context: Introduce any necessary background information or context to help the reader understand the relevance of the premise.
Use supporting evidence: Use evidence, examples, or quotes to support your premise. This will strengthen your argument and enhance the credibility of your essay.
Explain the connection: Clearly explain how the premise supports your thesis and how it fits into your overall argument.
Be logical: Ensure that your premises are logical and follow a clear progression. Each premise should build on the previous one and support your thesis in a logical way.

Remember, the presentation of your premises is what forms the backbone of your essay. It provides the necessary evidence and supporting arguments to prove your thesis and form a convincing and logical argument. Take the time to present your premises in a clear, concise, and convincing manner to ensure the success of your deductive essay.

7. Writing Deductive Essay Conclusion

Drawing a sound and logical conclusion is crucial when writing a deductive essay. It serves as the final section of the essay, where you summarize the premises presented and restate your thesis. The conclusion should leave the reader with a clear understanding of the argument and its significance. Here are some tips for writing a compelling conclusion for your deductive essay:

Summarize the main points: Use the conclusion to summarize the main premises presented in your essay. This will help reinforce the main points and provide a concise overview for the reader.
Restate your thesis: Restate your thesis in a clear and concise manner to remind the reader of the central argument of your essay. Use the conclusion to emphasize the importance of your thesis.
Provide a final thought: Provide a final thought or takeaway for the reader that ties in with the topic and argument of your essay.
Be confident: Be confident in your argument and use the conclusion to reinforce the strength of your deductive reasoning.
End strongly: End your conclusion with a strong statement, leaving the reader with a lasting impression of your writing.

Drawing a well-written conclusion is the final step in creating a successful deductive essay. By writing a strong conclusion that summarizes the premises and restates the thesis, you can ensure that the reader understands and appreciates the logical argument presented in your writing.

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Inductive Essays: Tips, Examples, And Topics
Elements of an Inductive Essay. Most of the time, an inductive essay has three main parts: an intro, body paragraphs, and a conclusion. The introduction should explain what the topic is about and show the evidence that will be looked at in the essay.It should also have a thesis statement that sums up the conclusion that will be drawn from the evidence.
Inductive Reasoning
Inductive reasoning is a method of drawing conclusions by going from the specific to the general. ... depending on the number and quality of observations and arguments used. Inductive generalization. ... You begin by using qualitative methods to explore the research topic, taking an inductive reasoning approach. You collect observations by ...
Inductive Essay Examples
Inductive Essay Examples. Unlike in a deductive essay, inductive texts explore the topic without arguing for the correctness of the hypothesis. Here you will provide evidence first and suggest your reasoning only in the concluding paragraph. In terms of structure, you move from the particular cases to the general principle.
Inductive & Deductive Reasoning
In the context of this deductive reasoning essay, an argument from analogy is one of the examples under deductive reasoning. The rule underlying this module is that in the case where P and Q are similar and have properties a, b, and c, object P has an extra property, "x.". Therefore, Q will automatically have the same extra property, "x ...
Inductive Reasoning: Definition, Examples, & Methods
Inductive reasoning is a type of reasoning that involves drawing general conclusions from specific observations. It's often called "bottom-up" reasoning because it starts with specific details and builds up to broader conclusions (The Decision Lab, n.d.). Here's a commonly used example.
What Is Inductive Reasoning?
Inductive reasoning involves making broad generalizations based on specific observations. Induction is used in various academic and professional settings, as well as informal everyday conversations and tasks. This type of reasoning is especially relevant to problems involving pattern recognition, prediction, and decision-making.
Inductive Reasoning
You may have come across inductive logic examples that come in a set of three statements. These start with one specific observation, add a general pattern, and end with a conclusion. Examples: Inductive reasoning. Stage. Example 1. Example 2. Specific observation. Nala is an orange cat and she purrs loudly.
Understanding Inductive Reasoning and Analogy
Inductive reasoning and analogy are both important tools for forming arguments in philosophical thinking. Inductive reasoning allows one to draw general conclusions from multiple observations, while analogy allows one to compare two or more objects and draw inferences from the comparison. Both approaches have strengths and weaknesses.
Inductive vs. Deductive Research Approach
Revised on June 22, 2023. The main difference between inductive and deductive reasoning is that inductive reasoning aims at developing a theory while deductive reasoning aims at testing an existing theory. In other words, inductive reasoning moves from specific observations to broad generalizations. Deductive reasoning works the other way around.
Inductive Logic
An inductive logic is a logic of evidential support. In a deductive logic, the premises of a valid deductive argument logically entail the conclusion, where logical entailment means that every logically possible state of affairs that makes the premises true must make the conclusion true as well. Thus, the premises of a valid deductive argument provide total support for the conclusion.
5.1: Inductive Logics
In this chapter, we will look at two very common types of inductive reasoning: arguments from analogy and inferences involving causation. The former are quite common in everyday life; the latter are the primary methods of scientific and medical research. Each type of reasoning exhibits certain patterns, and we will look at the general forms ...
Inductive Reasoning Essays
Inductive reasoning is a form of logical argumentation that seeks to establish a conclusion based on evidence. It is the process of drawing conclusions from specific observations or examples. In philosophy, inductive reasoning has been used for centuries in order to make general claims about the world and draw meaningful connections between ...
5: Inductive Logic I
5.1: Inductive Logics. In this chapter, we will look at two very common types of inductive reasoning: arguments from analogy and inferences involving causation. The former are quite common in everyday life; the latter are the primary methods of scientific and medical research. Each type of reasoning exhibits certain patterns, and we will look ...
13.3: Varieties of Inductive Arguments
The study of inductive logic is more complex than deductive logic, and it is not as well developed. It consists merely of several independent topical areas that focus on a particular kind of inductive argument. This section of the chapter briefly introduces some of the different kinds. Some inductive arguments are of more than one kind.
Deciphering Inductive Reasoning: Identifying Logic's Gems
It illustrates how inductive reasoning operates through specific examples, such as identifying patterns from observations, making predictive inferences, and drawing conclusions from empirical data. The essay emphasizes the nuanced nature of inductive reasoning, highlighting its role in guiding decision-making, scientific inquiry, and everyday life.
Inductive Reasoning and Inductive Arguments
Arguments where the goal (to achieve strong and reliable beliefs) is to provide the best available evidence for the conclusion; the nature of the inferential claim is such that it is unlikely that the premises are true and the conclusion false. Strong inductive arguments achieve this goal - providing the best available evidence.
15 Inductive Reasoning Examples (2024)
Well-Formulated Inductive Reasoning Examples. 1. Polling and Surveys. "We surveyed 1,000 people across the county and 520 of them said they will vote to re-elect the mayor. We estimate that 52% of the county will vote for the mayor and he will be re-elected.". Many statisticians make a living from conducting tried-and-true inductive ...
The Role of Inductive Reasoning in Everyday Life
This essay has provided several examples of applied inductive reasoning, such as weather projections and sales forecasting. Moreover, the challenges and limitations of inductive reasoning have been identified, including potential for inaccurate predictions, the influence of personal biases, and difficulties in collecting enough data.
Inductive Reasoning Essay Examples
Get your free examples of research papers and essays on Inductive Reasoning here. Only the A-papers by top-of-the-class students. Learn from the best! ... World of Writing Hub Blog Free Essay Writing Tools Quizzes and Tests Essay Topics Types of Essays Free Essay Examples Best Essay Writing Services. How It Works; About Us Who We Are ...
What Is a Premise?
In contrast, informal arguments typically rely on inductive reasoning. In these arguments, specific premises provide support for a broad conclusion, demonstrating that it is highly likely to be true. Premises in inductive reasoning example Premise: Every observed crow is black. Conclusion: It is likely that all crows are black.
9.4: Analogical Reasoning
The point is, the kinds of knock-down refutations that were possible for deductive arguments are not possible for inductive arguments. There is always room for further debate. [1] I'm sure someone could come up with some elaborate backstory for Alice according to which the location of the class somehow makes it more likely that she will do ...
Inductive Reasoning Essay Example
In some quarters, inductive reasoning is referred to as the scientific method, which consists of six steps: problem statement, evaluation of the problem, hypothesis statement, hypothesis testing, result analysis, stating the findings, and revision. These steps will ensure the credibility of the results and simplify the repetition process.
Using Inductive Reasoning Essay Example
In addition to this, inductive reasoning may not be handy in proving and providing arguments.... On the contrary, if I would be using inductive reasoning as a writer, I must observe a number of situations in which patterns exist.... lthough I read some dissertations that used both approach, I noticed that deductive reasoning is a valid form of proving....
How to Write a Deductive Essay
While both deductive and inductive writing involve reasoning and making arguments, there are key differences between the two approaches. Understanding these differences is crucial when learning how to write a deductive essay. ... 10 Deductive Essay Topics: The effects of climate change on the world's oceans. The link between social media and ...

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9.4: Analogical Reasoning

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