the scientific method is hypothesis driven

What Are The Steps Of The Scientific Method?

Julia Simkus

Editor at Simply Psychology

BA (Hons) Psychology, Princeton University

Julia Simkus is a graduate of Princeton University with a Bachelor of Arts in Psychology. She is currently studying for a Master's Degree in Counseling for Mental Health and Wellness in September 2023. Julia's research has been published in peer reviewed journals.

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Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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Science is not just knowledge. It is also a method for obtaining knowledge. Scientific understanding is organized into theories.

The scientific method is a step-by-step process used by researchers and scientists to determine if there is a relationship between two or more variables. Psychologists use this method to conduct psychological research, gather data, process information, and describe behaviors.

It involves careful observation, asking questions, formulating hypotheses, experimental testing, and refining hypotheses based on experimental findings.

How it is Used

The scientific method can be applied broadly in science across many different fields, such as chemistry, physics, geology, and psychology. In a typical application of this process, a researcher will develop a hypothesis, test this hypothesis, and then modify the hypothesis based on the outcomes of the experiment.

The process is then repeated with the modified hypothesis until the results align with the observed phenomena. Detailed steps of the scientific method are described below.

Keep in mind that the scientific method does not have to follow this fixed sequence of steps; rather, these steps represent a set of general principles or guidelines.

7 Steps of the Scientific Method

Psychology uses an empirical approach.

Empiricism (founded by John Locke) states that the only source of knowledge comes through our senses – e.g., sight, hearing, touch, etc.

Empirical evidence does not rely on argument or belief. Thus, empiricism is the view that all knowledge is based on or may come from direct observation and experience.

The empiricist approach of gaining knowledge through experience quickly became the scientific approach and greatly influenced the development of physics and chemistry in the 17th and 18th centuries.

Step 1: Make an Observation (Theory Construction)

Every researcher starts at the very beginning. Before diving in and exploring something, one must first determine what they will study – it seems simple enough!

By making observations, researchers can establish an area of interest. Once this topic of study has been chosen, a researcher should review existing literature to gain insight into what has already been tested and determine what questions remain unanswered.

This assessment will provide helpful information about what has already been comprehended about the specific topic and what questions remain, and if one can go and answer them.

Specifically, a literature review might implicate examining a substantial amount of documented material from academic journals to books dating back decades. The most appropriate information gathered by the researcher will be shown in the introduction section or abstract of the published study results.

The background material and knowledge will help the researcher with the first significant step in conducting a psychology study, which is formulating a research question.

This is the inductive phase of the scientific process. Observations yield information that is used to formulate theories as explanations. A theory is a well-developed set of ideas that propose an explanation for observed phenomena.

Inductive reasoning moves from specific premises to a general conclusion. It starts with observations of phenomena in the natural world and derives a general law.

Step 2: Ask a Question

Once a researcher has made observations and conducted background research, the next step is to ask a scientific question. A scientific question must be defined, testable, and measurable.

A useful approach to develop a scientific question is: “What is the effect of…?” or “How does X affect Y?”

To answer an experimental question, a researcher must identify two variables: the independent and dependent variables.

The independent variable is the variable manipulated (the cause), and the dependent variable is the variable being measured (the effect).

An example of a research question could be, “Is handwriting or typing more effective for retaining information?” Answering the research question and proposing a relationship between the two variables is discussed in the next step.

Step 3: Form a Hypothesis (Make Predictions)

A hypothesis is an educated guess about the relationship between two or more variables. A hypothesis is an attempt to answer your research question based on prior observation and background research. Theories tend to be too complex to be tested all at once; instead, researchers create hypotheses to test specific aspects of a theory.

For example, a researcher might ask about the connection between sleep and educational performance. Do students who get less sleep perform worse on tests at school?

It is crucial to think about different questions one might have about a particular topic to formulate a reasonable hypothesis. It would help if one also considered how one could investigate the causalities.

It is important that the hypothesis is both testable against reality and falsifiable. This means that it can be tested through an experiment and can be proven wrong.

The falsification principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory to be considered scientific, it must be able to be tested and conceivably proven false.

To test a hypothesis, we first assume that there is no difference between the populations from which the samples were taken. This is known as the null hypothesis and predicts that the independent variable will not influence the dependent variable.

Examples of “if…then…” Hypotheses:

• If one gets less than 6 hours of sleep, then one will do worse on tests than if one obtains more rest.
• If one drinks lots of water before going to bed, one will have to use the bathroom often at night.
• If one practices exercising and lighting weights, then one’s body will begin to build muscle.

The research hypothesis is often called the alternative hypothesis and predicts what change(s) will occur in the dependent variable when the independent variable is manipulated.

It states that the results are not due to chance and that they are significant in terms of supporting the theory being investigated.

Although one could state and write a scientific hypothesis in many ways, hypotheses are usually built like “if…then…” statements.

Step 4: Run an Experiment (Gather Data)

The next step in the scientific method is to test your hypothesis and collect data. A researcher will design an experiment to test the hypothesis and gather data that will either support or refute the hypothesis.

The exact research methods used to examine a hypothesis depend on what is being studied. A psychologist might utilize two primary forms of research, experimental research, and descriptive research.

The scientific method is objective in that researchers do not let preconceived ideas or biases influence the collection of data and is systematic in that experiments are conducted in a logical way.

Experimental Research

Experimental research is used to investigate cause-and-effect associations between two or more variables. This type of research systematically controls an independent variable and measures its effect on a specified dependent variable.

Experimental research involves manipulating an independent variable and measuring the effect(s) on the dependent variable. Repeating the experiment multiple times is important to confirm that your results are accurate and consistent.

One of the significant advantages of this method is that it permits researchers to determine if changes in one variable cause shifts in each other.

While experiments in psychology typically have many moving parts (and can be relatively complex), an easy investigation is rather fundamental. Still, it does allow researchers to specify cause-and-effect associations between variables.

Most simple experiments use a control group, which involves those who do not receive the treatment, and an experimental group, which involves those who do receive the treatment.

An example of experimental research would be when a pharmaceutical company wants to test a new drug. They give one group a placebo (control group) and the other the actual pill (experimental group).

Descriptive Research

Descriptive research is generally used when it is challenging or even impossible to control the variables in question. Examples of descriptive analysis include naturalistic observation, case studies , and correlation studies .

One example of descriptive research includes phone surveys that marketers often use. While they typically do not allow researchers to identify cause and effect, correlational studies are quite common in psychology research. They make it possible to spot associations between distinct variables and measure the solidity of those relationships.

Step 5: Analyze the Data and Draw Conclusions

Once a researcher has designed and done the investigation and collected sufficient data, it is time to inspect this gathered information and judge what has been found. Researchers can summarize the data, interpret the results, and draw conclusions based on this evidence using analyses and statistics.

Upon completion of the experiment, you can collect your measurements and analyze the data using statistics. Based on the outcomes, you will either reject or confirm your hypothesis.

Analyze the Data

So, how does a researcher determine what the results of their study mean? Statistical analysis can either support or refute a researcher’s hypothesis and can also be used to determine if the conclusions are statistically significant.

When outcomes are said to be “statistically significant,” it is improbable that these results are due to luck or chance. Based on these observations, investigators must then determine what the results mean.

An experiment will support a hypothesis in some circumstances, but sometimes it fails to be truthful in other cases.

What occurs if the developments of a psychology investigation do not endorse the researcher’s hypothesis? It does mean that the study was worthless. Simply because the findings fail to defend the researcher’s hypothesis does not mean that the examination is not helpful or instructive.

This kind of research plays a vital role in supporting scientists in developing unexplored questions and hypotheses to investigate in the future. After decisions have been made, the next step is to communicate the results with the rest of the scientific community.

This is an integral part of the process because it contributes to the general knowledge base and can assist other scientists in finding new research routes to explore.

If the hypothesis is not supported, a researcher should acknowledge the experiment’s results, formulate a new hypothesis, and develop a new experiment.

We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist that could refute a theory.

Draw Conclusions and Interpret the Data

When the empirical observations disagree with the hypothesis, a number of possibilities must be considered. It might be that the theory is incorrect, in which case it needs altering, so it fully explains the data.

Alternatively, it might be that the hypothesis was poorly derived from the original theory, in which case the scientists were expecting the wrong thing to happen.

It might also be that the research was poorly conducted, or used an inappropriate method, or there were factors in play that the researchers did not consider. This will begin the process of the scientific method again.

If the hypothesis is supported, the researcher can find more evidence to support their hypothesis or look for counter-evidence to strengthen their hypothesis further.

In either scenario, the researcher should share their results with the greater scientific community.

Step 6: Share Your Results

One of the final stages of the research cycle involves the publication of the research. Once the report is written, the researcher(s) may submit the work for publication in an appropriate journal.

Usually, this is done by writing up a study description and publishing the article in a professional or academic journal. The studies and conclusions of psychological work can be seen in peer-reviewed journals such as  Developmental Psychology , Psychological Bulletin, the  Journal of Social Psychology, and numerous others.

Scientists should report their findings by writing up a description of their study and any subsequent findings. This enables other researchers to build upon the present research or replicate the results.

As outlined by the American Psychological Association (APA), there is a typical structure of a journal article that follows a specified format. In these articles, researchers:

• Supply a brief narrative and background on previous research
• Give their hypothesis
• Specify who participated in the study and how they were chosen
• Provide operational definitions for each variable
• Explain the measures and methods used to collect data
• Describe how the data collected was interpreted
• Discuss what the outcomes mean

A detailed record of psychological studies and all scientific studies is vital to clearly explain the steps and procedures used throughout the study. So that other researchers can try this experiment too and replicate the results.

The editorial process utilized by academic and professional journals guarantees that each submitted article undergoes a thorough peer review to help assure that the study is scientifically sound. Once published, the investigation becomes another piece of the current puzzle of our knowledge “base” on that subject.

This last step is important because all results, whether they supported or did not support the hypothesis, can contribute to the scientific community. Publication of empirical observations leads to more ideas that are tested against the real world, and so on. In this sense, the scientific process is circular.

The editorial process utilized by academic and professional journals guarantees that each submitted article undergoes a thorough peer review to help assure that the study is scientifically sound.

Once published, the investigation becomes another piece of the current puzzle of our knowledge “base” on that subject.

By replicating studies, psychologists can reduce errors, validate theories, and gain a stronger understanding of a particular topic.

Step 7: Repeat the Scientific Method (Iteration)

Now, if one’s hypothesis turns out to be accurate, find more evidence or find counter-evidence. If one’s hypothesis is false, create a new hypothesis or try again.

One may wish to revise their first hypothesis to make a more niche experiment to design or a different specific question to test.

The amazingness of the scientific method is that it is a comprehensive and straightforward process that scientists, and everyone, can utilize over and over again.

So, draw conclusions and repeat because the scientific method is never-ending, and no result is ever considered perfect.

The scientific method is a process of:

• Making an observation.
• Forming a hypothesis.
• Making a prediction.
• Experimenting to test the hypothesis.

The procedure of repeating the scientific method is crucial to science and all fields of human knowledge.

Further Information

• Karl Popper – Falsification
• Thomas – Kuhn Paradigm Shift
• Positivism in Sociology: Definition, Theory & Examples
• Is Psychology a Science?
• Psychology as a Science (PDF)

List the 6 steps of the scientific methods in order

• Make an observation (theory construction)
• Ask a question. A scientific question must be defined, testable, and measurable.
• Form a hypothesis (make predictions)
• Run an experiment to test the hypothesis (gather data)
• Analyze the data and draw conclusions
• Share your results so that other researchers can make new hypotheses

What is the first step of the scientific method?

The first step of the scientific method is making an observation. This involves noticing and describing a phenomenon or group of phenomena that one finds interesting and wishes to explain.

Observations can occur in a natural setting or within the confines of a laboratory. The key point is that the observation provides the initial question or problem that the rest of the scientific method seeks to answer or solve.

What is the scientific method?

The scientific method is a step-by-step process that investigators can follow to determine if there is a causal connection between two or more variables.

Psychologists and other scientists regularly suggest motivations for human behavior. On a more casual level, people judge other people’s intentions, incentives, and actions daily.

While our standard assessments of human behavior are subjective and anecdotal, researchers use the scientific method to study psychology objectively and systematically.

All utilize a scientific method to study distinct aspects of people’s thinking and behavior. This process allows scientists to analyze and understand various psychological phenomena, but it also provides investigators and others a way to disseminate and debate the results of their studies.

The outcomes of these studies are often noted in popular media, which leads numerous to think about how or why researchers came to the findings they did.

Why Use the Six Steps of the Scientific Method

The goal of scientists is to understand better the world that surrounds us. Scientific research is the most critical tool for navigating and learning about our complex world.

Without it, we would be compelled to rely solely on intuition, other people’s power, and luck. We can eliminate our preconceived concepts and superstitions through methodical scientific research and gain an objective sense of ourselves and our world.

All psychological studies aim to explain, predict, and even control or impact mental behaviors or processes. So, psychologists use and repeat the scientific method (and its six steps) to perform and record essential psychological research.

So, psychologists focus on understanding behavior and the cognitive (mental) and physiological (body) processes underlying behavior.

In the real world, people use to understand the behavior of others, such as intuition and personal experience. The hallmark of scientific research is evidence to support a claim.

Scientific knowledge is empirical, meaning it is grounded in objective, tangible evidence that can be observed repeatedly, regardless of who is watching.

The scientific method is crucial because it minimizes the impact of bias or prejudice on the experimenter. Regardless of how hard one tries, even the best-intentioned scientists can’t escape discrimination. can’t

It stems from personal opinions and cultural beliefs, meaning any mortal filters data based on one’s experience. Sadly, this “filtering” process can cause a scientist to favor one outcome over another.

For an everyday person trying to solve a minor issue at home or work, succumbing to these biases is not such a big deal; in fact, most times, it is important.

But in the scientific community, where results must be inspected and reproduced, bias or discrimination must be avoided.

When to Use the Six Steps of the Scientific Method ?

One can use the scientific method anytime, anywhere! From the smallest conundrum to solving global problems, it is a process that can be applied to any science and any investigation.

Even if you are not considered a “scientist,” you will be surprised to know that people of all disciplines use it for all kinds of dilemmas.

Try to catch yourself next time you come by a question and see how you subconsciously or consciously use the scientific method.

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Scientific Method

Science is an enormously successful human enterprise. The study of scientific method is the attempt to discern the activities by which that success is achieved. Among the activities often identified as characteristic of science are systematic observation and experimentation, inductive and deductive reasoning, and the formation and testing of hypotheses and theories. How these are carried out in detail can vary greatly, but characteristics like these have been looked to as a way of demarcating scientific activity from non-science, where only enterprises which employ some canonical form of scientific method or methods should be considered science (see also the entry on science and pseudo-science ). Others have questioned whether there is anything like a fixed toolkit of methods which is common across science and only science. Some reject privileging one view of method as part of rejecting broader views about the nature of science, such as naturalism (Dupré 2004); some reject any restriction in principle (pluralism).

Scientific method should be distinguished from the aims and products of science, such as knowledge, predictions, or control. Methods are the means by which those goals are achieved. Scientific method should also be distinguished from meta-methodology, which includes the values and justifications behind a particular characterization of scientific method (i.e., a methodology) — values such as objectivity, reproducibility, simplicity, or past successes. Methodological rules are proposed to govern method and it is a meta-methodological question whether methods obeying those rules satisfy given values. Finally, method is distinct, to some degree, from the detailed and contextual practices through which methods are implemented. The latter might range over: specific laboratory techniques; mathematical formalisms or other specialized languages used in descriptions and reasoning; technological or other material means; ways of communicating and sharing results, whether with other scientists or with the public at large; or the conventions, habits, enforced customs, and institutional controls over how and what science is carried out.

While it is important to recognize these distinctions, their boundaries are fuzzy. Hence, accounts of method cannot be entirely divorced from their methodological and meta-methodological motivations or justifications, Moreover, each aspect plays a crucial role in identifying methods. Disputes about method have therefore played out at the detail, rule, and meta-rule levels. Changes in beliefs about the certainty or fallibility of scientific knowledge, for instance (which is a meta-methodological consideration of what we can hope for methods to deliver), have meant different emphases on deductive and inductive reasoning, or on the relative importance attached to reasoning over observation (i.e., differences over particular methods.) Beliefs about the role of science in society will affect the place one gives to values in scientific method.

The issue which has shaped debates over scientific method the most in the last half century is the question of how pluralist do we need to be about method? Unificationists continue to hold out for one method essential to science; nihilism is a form of radical pluralism, which considers the effectiveness of any methodological prescription to be so context sensitive as to render it not explanatory on its own. Some middle degree of pluralism regarding the methods embodied in scientific practice seems appropriate. But the details of scientific practice vary with time and place, from institution to institution, across scientists and their subjects of investigation. How significant are the variations for understanding science and its success? How much can method be abstracted from practice? This entry describes some of the attempts to characterize scientific method or methods, as well as arguments for a more context-sensitive approach to methods embedded in actual scientific practices.

1. Overview and organizing themes

2. historical review: aristotle to mill, 3.1 logical constructionism and operationalism, 3.2. h-d as a logic of confirmation, 3.3. popper and falsificationism, 3.4 meta-methodology and the end of method, 4. statistical methods for hypothesis testing, 5.1 creative and exploratory practices.

• 5.2 Computer methods and the ‘new ways’ of doing science

6.1 “The scientific method” in science education and as seen by scientists

6.2 privileged methods and ‘gold standards’, 6.3 scientific method in the court room, 6.4 deviating practices, 7. conclusion, other internet resources, related entries.

This entry could have been given the title Scientific Methods and gone on to fill volumes, or it could have been extremely short, consisting of a brief summary rejection of the idea that there is any such thing as a unique Scientific Method at all. Both unhappy prospects are due to the fact that scientific activity varies so much across disciplines, times, places, and scientists that any account which manages to unify it all will either consist of overwhelming descriptive detail, or trivial generalizations.

The choice of scope for the present entry is more optimistic, taking a cue from the recent movement in philosophy of science toward a greater attention to practice: to what scientists actually do. This “turn to practice” can be seen as the latest form of studies of methods in science, insofar as it represents an attempt at understanding scientific activity, but through accounts that are neither meant to be universal and unified, nor singular and narrowly descriptive. To some extent, different scientists at different times and places can be said to be using the same method even though, in practice, the details are different.

Whether the context in which methods are carried out is relevant, or to what extent, will depend largely on what one takes the aims of science to be and what one’s own aims are. For most of the history of scientific methodology the assumption has been that the most important output of science is knowledge and so the aim of methodology should be to discover those methods by which scientific knowledge is generated.

Science was seen to embody the most successful form of reasoning (but which form?) to the most certain knowledge claims (but how certain?) on the basis of systematically collected evidence (but what counts as evidence, and should the evidence of the senses take precedence, or rational insight?) Section 2 surveys some of the history, pointing to two major themes. One theme is seeking the right balance between observation and reasoning (and the attendant forms of reasoning which employ them); the other is how certain scientific knowledge is or can be.

Section 3 turns to 20 th century debates on scientific method. In the second half of the 20 th century the epistemic privilege of science faced several challenges and many philosophers of science abandoned the reconstruction of the logic of scientific method. Views changed significantly regarding which functions of science ought to be captured and why. For some, the success of science was better identified with social or cultural features. Historical and sociological turns in the philosophy of science were made, with a demand that greater attention be paid to the non-epistemic aspects of science, such as sociological, institutional, material, and political factors. Even outside of those movements there was an increased specialization in the philosophy of science, with more and more focus on specific fields within science. The combined upshot was very few philosophers arguing any longer for a grand unified methodology of science. Sections 3 and 4 surveys the main positions on scientific method in 20 th century philosophy of science, focusing on where they differ in their preference for confirmation or falsification or for waiving the idea of a special scientific method altogether.

In recent decades, attention has primarily been paid to scientific activities traditionally falling under the rubric of method, such as experimental design and general laboratory practice, the use of statistics, the construction and use of models and diagrams, interdisciplinary collaboration, and science communication. Sections 4–6 attempt to construct a map of the current domains of the study of methods in science.

As these sections illustrate, the question of method is still central to the discourse about science. Scientific method remains a topic for education, for science policy, and for scientists. It arises in the public domain where the demarcation or status of science is at issue. Some philosophers have recently returned, therefore, to the question of what it is that makes science a unique cultural product. This entry will close with some of these recent attempts at discerning and encapsulating the activities by which scientific knowledge is achieved.

Attempting a history of scientific method compounds the vast scope of the topic. This section briefly surveys the background to modern methodological debates. What can be called the classical view goes back to antiquity, and represents a point of departure for later divergences. [ 1 ]

We begin with a point made by Laudan (1968) in his historical survey of scientific method:

Perhaps the most serious inhibition to the emergence of the history of theories of scientific method as a respectable area of study has been the tendency to conflate it with the general history of epistemology, thereby assuming that the narrative categories and classificatory pigeon-holes applied to the latter are also basic to the former. (1968: 5)

To see knowledge about the natural world as falling under knowledge more generally is an understandable conflation. Histories of theories of method would naturally employ the same narrative categories and classificatory pigeon holes. An important theme of the history of epistemology, for example, is the unification of knowledge, a theme reflected in the question of the unification of method in science. Those who have identified differences in kinds of knowledge have often likewise identified different methods for achieving that kind of knowledge (see the entry on the unity of science ).

Different views on what is known, how it is known, and what can be known are connected. Plato distinguished the realms of things into the visible and the intelligible ( The Republic , 510a, in Cooper 1997). Only the latter, the Forms, could be objects of knowledge. The intelligible truths could be known with the certainty of geometry and deductive reasoning. What could be observed of the material world, however, was by definition imperfect and deceptive, not ideal. The Platonic way of knowledge therefore emphasized reasoning as a method, downplaying the importance of observation. Aristotle disagreed, locating the Forms in the natural world as the fundamental principles to be discovered through the inquiry into nature ( Metaphysics Z , in Barnes 1984).

Aristotle is recognized as giving the earliest systematic treatise on the nature of scientific inquiry in the western tradition, one which embraced observation and reasoning about the natural world. In the Prior and Posterior Analytics , Aristotle reflects first on the aims and then the methods of inquiry into nature. A number of features can be found which are still considered by most to be essential to science. For Aristotle, empiricism, careful observation (but passive observation, not controlled experiment), is the starting point. The aim is not merely recording of facts, though. For Aristotle, science ( epistêmê ) is a body of properly arranged knowledge or learning—the empirical facts, but also their ordering and display are of crucial importance. The aims of discovery, ordering, and display of facts partly determine the methods required of successful scientific inquiry. Also determinant is the nature of the knowledge being sought, and the explanatory causes proper to that kind of knowledge (see the discussion of the four causes in the entry on Aristotle on causality ).

In addition to careful observation, then, scientific method requires a logic as a system of reasoning for properly arranging, but also inferring beyond, what is known by observation. Methods of reasoning may include induction, prediction, or analogy, among others. Aristotle’s system (along with his catalogue of fallacious reasoning) was collected under the title the Organon . This title would be echoed in later works on scientific reasoning, such as Novum Organon by Francis Bacon, and Novum Organon Restorum by William Whewell (see below). In Aristotle’s Organon reasoning is divided primarily into two forms, a rough division which persists into modern times. The division, known most commonly today as deductive versus inductive method, appears in other eras and methodologies as analysis/​synthesis, non-ampliative/​ampliative, or even confirmation/​verification. The basic idea is there are two “directions” to proceed in our methods of inquiry: one away from what is observed, to the more fundamental, general, and encompassing principles; the other, from the fundamental and general to instances or implications of principles.

The basic aim and method of inquiry identified here can be seen as a theme running throughout the next two millennia of reflection on the correct way to seek after knowledge: carefully observe nature and then seek rules or principles which explain or predict its operation. The Aristotelian corpus provided the framework for a commentary tradition on scientific method independent of science itself (cosmos versus physics.) During the medieval period, figures such as Albertus Magnus (1206–1280), Thomas Aquinas (1225–1274), Robert Grosseteste (1175–1253), Roger Bacon (1214/1220–1292), William of Ockham (1287–1347), Andreas Vesalius (1514–1546), Giacomo Zabarella (1533–1589) all worked to clarify the kind of knowledge obtainable by observation and induction, the source of justification of induction, and best rules for its application. [ 2 ] Many of their contributions we now think of as essential to science (see also Laudan 1968). As Aristotle and Plato had employed a framework of reasoning either “to the forms” or “away from the forms”, medieval thinkers employed directions away from the phenomena or back to the phenomena. In analysis, a phenomena was examined to discover its basic explanatory principles; in synthesis, explanations of a phenomena were constructed from first principles.

During the Scientific Revolution these various strands of argument, experiment, and reason were forged into a dominant epistemic authority. The 16 th –18 th centuries were a period of not only dramatic advance in knowledge about the operation of the natural world—advances in mechanical, medical, biological, political, economic explanations—but also of self-awareness of the revolutionary changes taking place, and intense reflection on the source and legitimation of the method by which the advances were made. The struggle to establish the new authority included methodological moves. The Book of Nature, according to the metaphor of Galileo Galilei (1564–1642) or Francis Bacon (1561–1626), was written in the language of mathematics, of geometry and number. This motivated an emphasis on mathematical description and mechanical explanation as important aspects of scientific method. Through figures such as Henry More and Ralph Cudworth, a neo-Platonic emphasis on the importance of metaphysical reflection on nature behind appearances, particularly regarding the spiritual as a complement to the purely mechanical, remained an important methodological thread of the Scientific Revolution (see the entries on Cambridge platonists ; Boyle ; Henry More ; Galileo ).

In Novum Organum (1620), Bacon was critical of the Aristotelian method for leaping from particulars to universals too quickly. The syllogistic form of reasoning readily mixed those two types of propositions. Bacon aimed at the invention of new arts, principles, and directions. His method would be grounded in methodical collection of observations, coupled with correction of our senses (and particularly, directions for the avoidance of the Idols, as he called them, kinds of systematic errors to which naïve observers are prone.) The community of scientists could then climb, by a careful, gradual and unbroken ascent, to reliable general claims.

Bacon’s method has been criticized as impractical and too inflexible for the practicing scientist. Whewell would later criticize Bacon in his System of Logic for paying too little attention to the practices of scientists. It is hard to find convincing examples of Bacon’s method being put in to practice in the history of science, but there are a few who have been held up as real examples of 16 th century scientific, inductive method, even if not in the rigid Baconian mold: figures such as Robert Boyle (1627–1691) and William Harvey (1578–1657) (see the entry on Bacon ).

It is to Isaac Newton (1642–1727), however, that historians of science and methodologists have paid greatest attention. Given the enormous success of his Principia Mathematica and Opticks , this is understandable. The study of Newton’s method has had two main thrusts: the implicit method of the experiments and reasoning presented in the Opticks, and the explicit methodological rules given as the Rules for Philosophising (the Regulae) in Book III of the Principia . [ 3 ] Newton’s law of gravitation, the linchpin of his new cosmology, broke with explanatory conventions of natural philosophy, first for apparently proposing action at a distance, but more generally for not providing “true”, physical causes. The argument for his System of the World ( Principia , Book III) was based on phenomena, not reasoned first principles. This was viewed (mainly on the continent) as insufficient for proper natural philosophy. The Regulae counter this objection, re-defining the aims of natural philosophy by re-defining the method natural philosophers should follow. (See the entry on Newton’s philosophy .)

To his list of methodological prescriptions should be added Newton’s famous phrase “ hypotheses non fingo ” (commonly translated as “I frame no hypotheses”.) The scientist was not to invent systems but infer explanations from observations, as Bacon had advocated. This would come to be known as inductivism. In the century after Newton, significant clarifications of the Newtonian method were made. Colin Maclaurin (1698–1746), for instance, reconstructed the essential structure of the method as having complementary analysis and synthesis phases, one proceeding away from the phenomena in generalization, the other from the general propositions to derive explanations of new phenomena. Denis Diderot (1713–1784) and editors of the Encyclopédie did much to consolidate and popularize Newtonianism, as did Francesco Algarotti (1721–1764). The emphasis was often the same, as much on the character of the scientist as on their process, a character which is still commonly assumed. The scientist is humble in the face of nature, not beholden to dogma, obeys only his eyes, and follows the truth wherever it leads. It was certainly Voltaire (1694–1778) and du Chatelet (1706–1749) who were most influential in propagating the latter vision of the scientist and their craft, with Newton as hero. Scientific method became a revolutionary force of the Enlightenment. (See also the entries on Newton , Leibniz , Descartes , Boyle , Hume , enlightenment , as well as Shank 2008 for a historical overview.)

Not all 18 th century reflections on scientific method were so celebratory. Famous also are George Berkeley’s (1685–1753) attack on the mathematics of the new science, as well as the over-emphasis of Newtonians on observation; and David Hume’s (1711–1776) undermining of the warrant offered for scientific claims by inductive justification (see the entries on: George Berkeley ; David Hume ; Hume’s Newtonianism and Anti-Newtonianism ). Hume’s problem of induction motivated Immanuel Kant (1724–1804) to seek new foundations for empirical method, though as an epistemic reconstruction, not as any set of practical guidelines for scientists. Both Hume and Kant influenced the methodological reflections of the next century, such as the debate between Mill and Whewell over the certainty of inductive inferences in science.

The debate between John Stuart Mill (1806–1873) and William Whewell (1794–1866) has become the canonical methodological debate of the 19 th century. Although often characterized as a debate between inductivism and hypothetico-deductivism, the role of the two methods on each side is actually more complex. On the hypothetico-deductive account, scientists work to come up with hypotheses from which true observational consequences can be deduced—hence, hypothetico-deductive. Because Whewell emphasizes both hypotheses and deduction in his account of method, he can be seen as a convenient foil to the inductivism of Mill. However, equally if not more important to Whewell’s portrayal of scientific method is what he calls the “fundamental antithesis”. Knowledge is a product of the objective (what we see in the world around us) and subjective (the contributions of our mind to how we perceive and understand what we experience, which he called the Fundamental Ideas). Both elements are essential according to Whewell, and he was therefore critical of Kant for too much focus on the subjective, and John Locke (1632–1704) and Mill for too much focus on the senses. Whewell’s fundamental ideas can be discipline relative. An idea can be fundamental even if it is necessary for knowledge only within a given scientific discipline (e.g., chemical affinity for chemistry). This distinguishes fundamental ideas from the forms and categories of intuition of Kant. (See the entry on Whewell .)

Clarifying fundamental ideas would therefore be an essential part of scientific method and scientific progress. Whewell called this process “Discoverer’s Induction”. It was induction, following Bacon or Newton, but Whewell sought to revive Bacon’s account by emphasising the role of ideas in the clear and careful formulation of inductive hypotheses. Whewell’s induction is not merely the collecting of objective facts. The subjective plays a role through what Whewell calls the Colligation of Facts, a creative act of the scientist, the invention of a theory. A theory is then confirmed by testing, where more facts are brought under the theory, called the Consilience of Inductions. Whewell felt that this was the method by which the true laws of nature could be discovered: clarification of fundamental concepts, clever invention of explanations, and careful testing. Mill, in his critique of Whewell, and others who have cast Whewell as a fore-runner of the hypothetico-deductivist view, seem to have under-estimated the importance of this discovery phase in Whewell’s understanding of method (Snyder 1997a,b, 1999). Down-playing the discovery phase would come to characterize methodology of the early 20 th century (see section 3 ).

Mill, in his System of Logic , put forward a narrower view of induction as the essence of scientific method. For Mill, induction is the search first for regularities among events. Among those regularities, some will continue to hold for further observations, eventually gaining the status of laws. One can also look for regularities among the laws discovered in a domain, i.e., for a law of laws. Which “law law” will hold is time and discipline dependent and open to revision. One example is the Law of Universal Causation, and Mill put forward specific methods for identifying causes—now commonly known as Mill’s methods. These five methods look for circumstances which are common among the phenomena of interest, those which are absent when the phenomena are, or those for which both vary together. Mill’s methods are still seen as capturing basic intuitions about experimental methods for finding the relevant explanatory factors ( System of Logic (1843), see Mill entry). The methods advocated by Whewell and Mill, in the end, look similar. Both involve inductive generalization to covering laws. They differ dramatically, however, with respect to the necessity of the knowledge arrived at; that is, at the meta-methodological level (see the entries on Whewell and Mill entries).

3. Logic of method and critical responses

The quantum and relativistic revolutions in physics in the early 20 th century had a profound effect on methodology. Conceptual foundations of both theories were taken to show the defeasibility of even the most seemingly secure intuitions about space, time and bodies. Certainty of knowledge about the natural world was therefore recognized as unattainable. Instead a renewed empiricism was sought which rendered science fallible but still rationally justifiable.

Analyses of the reasoning of scientists emerged, according to which the aspects of scientific method which were of primary importance were the means of testing and confirming of theories. A distinction in methodology was made between the contexts of discovery and justification. The distinction could be used as a wedge between the particularities of where and how theories or hypotheses are arrived at, on the one hand, and the underlying reasoning scientists use (whether or not they are aware of it) when assessing theories and judging their adequacy on the basis of the available evidence. By and large, for most of the 20 th century, philosophy of science focused on the second context, although philosophers differed on whether to focus on confirmation or refutation as well as on the many details of how confirmation or refutation could or could not be brought about. By the mid-20 th century these attempts at defining the method of justification and the context distinction itself came under pressure. During the same period, philosophy of science developed rapidly, and from section 4 this entry will therefore shift from a primarily historical treatment of the scientific method towards a primarily thematic one.

Advances in logic and probability held out promise of the possibility of elaborate reconstructions of scientific theories and empirical method, the best example being Rudolf Carnap’s The Logical Structure of the World (1928). Carnap attempted to show that a scientific theory could be reconstructed as a formal axiomatic system—that is, a logic. That system could refer to the world because some of its basic sentences could be interpreted as observations or operations which one could perform to test them. The rest of the theoretical system, including sentences using theoretical or unobservable terms (like electron or force) would then either be meaningful because they could be reduced to observations, or they had purely logical meanings (called analytic, like mathematical identities). This has been referred to as the verifiability criterion of meaning. According to the criterion, any statement not either analytic or verifiable was strictly meaningless. Although the view was endorsed by Carnap in 1928, he would later come to see it as too restrictive (Carnap 1956). Another familiar version of this idea is operationalism of Percy William Bridgman. In The Logic of Modern Physics (1927) Bridgman asserted that every physical concept could be defined in terms of the operations one would perform to verify the application of that concept. Making good on the operationalisation of a concept even as simple as length, however, can easily become enormously complex (for measuring very small lengths, for instance) or impractical (measuring large distances like light years.)

Carl Hempel’s (1950, 1951) criticisms of the verifiability criterion of meaning had enormous influence. He pointed out that universal generalizations, such as most scientific laws, were not strictly meaningful on the criterion. Verifiability and operationalism both seemed too restrictive to capture standard scientific aims and practice. The tenuous connection between these reconstructions and actual scientific practice was criticized in another way. In both approaches, scientific methods are instead recast in methodological roles. Measurements, for example, were looked to as ways of giving meanings to terms. The aim of the philosopher of science was not to understand the methods per se , but to use them to reconstruct theories, their meanings, and their relation to the world. When scientists perform these operations, however, they will not report that they are doing them to give meaning to terms in a formal axiomatic system. This disconnect between methodology and the details of actual scientific practice would seem to violate the empiricism the Logical Positivists and Bridgman were committed to. The view that methodology should correspond to practice (to some extent) has been called historicism, or intuitionism. We turn to these criticisms and responses in section 3.4 . [ 4 ]

Positivism also had to contend with the recognition that a purely inductivist approach, along the lines of Bacon-Newton-Mill, was untenable. There was no pure observation, for starters. All observation was theory laden. Theory is required to make any observation, therefore not all theory can be derived from observation alone. (See the entry on theory and observation in science .) Even granting an observational basis, Hume had already pointed out that one could not deductively justify inductive conclusions without begging the question by presuming the success of the inductive method. Likewise, positivist attempts at analyzing how a generalization can be confirmed by observations of its instances were subject to a number of criticisms. Goodman (1965) and Hempel (1965) both point to paradoxes inherent in standard accounts of confirmation. Recent attempts at explaining how observations can serve to confirm a scientific theory are discussed in section 4 below.

The standard starting point for a non-inductive analysis of the logic of confirmation is known as the Hypothetico-Deductive (H-D) method. In its simplest form, a sentence of a theory which expresses some hypothesis is confirmed by its true consequences. As noted in section 2 , this method had been advanced by Whewell in the 19 th century, as well as Nicod (1924) and others in the 20 th century. Often, Hempel’s (1966) description of the H-D method, illustrated by the case of Semmelweiss’ inferential procedures in establishing the cause of childbed fever, has been presented as a key account of H-D as well as a foil for criticism of the H-D account of confirmation (see, for example, Lipton’s (2004) discussion of inference to the best explanation; also the entry on confirmation ). Hempel described Semmelsweiss’ procedure as examining various hypotheses explaining the cause of childbed fever. Some hypotheses conflicted with observable facts and could be rejected as false immediately. Others needed to be tested experimentally by deducing which observable events should follow if the hypothesis were true (what Hempel called the test implications of the hypothesis), then conducting an experiment and observing whether or not the test implications occurred. If the experiment showed the test implication to be false, the hypothesis could be rejected. If the experiment showed the test implications to be true, however, this did not prove the hypothesis true. The confirmation of a test implication does not verify a hypothesis, though Hempel did allow that “it provides at least some support, some corroboration or confirmation for it” (Hempel 1966: 8). The degree of this support then depends on the quantity, variety and precision of the supporting evidence.

Another approach that took off from the difficulties with inductive inference was Karl Popper’s critical rationalism or falsificationism (Popper 1959, 1963). Falsification is deductive and similar to H-D in that it involves scientists deducing observational consequences from the hypothesis under test. For Popper, however, the important point was not the degree of confirmation that successful prediction offered to a hypothesis. The crucial thing was the logical asymmetry between confirmation, based on inductive inference, and falsification, which can be based on a deductive inference. (This simple opposition was later questioned, by Lakatos, among others. See the entry on historicist theories of scientific rationality. )

Popper stressed that, regardless of the amount of confirming evidence, we can never be certain that a hypothesis is true without committing the fallacy of affirming the consequent. Instead, Popper introduced the notion of corroboration as a measure for how well a theory or hypothesis has survived previous testing—but without implying that this is also a measure for the probability that it is true.

Popper was also motivated by his doubts about the scientific status of theories like the Marxist theory of history or psycho-analysis, and so wanted to demarcate between science and pseudo-science. Popper saw this as an importantly different distinction than demarcating science from metaphysics. The latter demarcation was the primary concern of many logical empiricists. Popper used the idea of falsification to draw a line instead between pseudo and proper science. Science was science because its method involved subjecting theories to rigorous tests which offered a high probability of failing and thus refuting the theory.

A commitment to the risk of failure was important. Avoiding falsification could be done all too easily. If a consequence of a theory is inconsistent with observations, an exception can be added by introducing auxiliary hypotheses designed explicitly to save the theory, so-called ad hoc modifications. This Popper saw done in pseudo-science where ad hoc theories appeared capable of explaining anything in their field of application. In contrast, science is risky. If observations showed the predictions from a theory to be wrong, the theory would be refuted. Hence, scientific hypotheses must be falsifiable. Not only must there exist some possible observation statement which could falsify the hypothesis or theory, were it observed, (Popper called these the hypothesis’ potential falsifiers) it is crucial to the Popperian scientific method that such falsifications be sincerely attempted on a regular basis.

The more potential falsifiers of a hypothesis, the more falsifiable it would be, and the more the hypothesis claimed. Conversely, hypotheses without falsifiers claimed very little or nothing at all. Originally, Popper thought that this meant the introduction of ad hoc hypotheses only to save a theory should not be countenanced as good scientific method. These would undermine the falsifiabililty of a theory. However, Popper later came to recognize that the introduction of modifications (immunizations, he called them) was often an important part of scientific development. Responding to surprising or apparently falsifying observations often generated important new scientific insights. Popper’s own example was the observed motion of Uranus which originally did not agree with Newtonian predictions. The ad hoc hypothesis of an outer planet explained the disagreement and led to further falsifiable predictions. Popper sought to reconcile the view by blurring the distinction between falsifiable and not falsifiable, and speaking instead of degrees of testability (Popper 1985: 41f.).

From the 1960s on, sustained meta-methodological criticism emerged that drove philosophical focus away from scientific method. A brief look at those criticisms follows, with recommendations for further reading at the end of the entry.

Thomas Kuhn’s The Structure of Scientific Revolutions (1962) begins with a well-known shot across the bow for philosophers of science:

History, if viewed as a repository for more than anecdote or chronology, could produce a decisive transformation in the image of science by which we are now possessed. (1962: 1)

The image Kuhn thought needed transforming was the a-historical, rational reconstruction sought by many of the Logical Positivists, though Carnap and other positivists were actually quite sympathetic to Kuhn’s views. (See the entry on the Vienna Circle .) Kuhn shares with other of his contemporaries, such as Feyerabend and Lakatos, a commitment to a more empirical approach to philosophy of science. Namely, the history of science provides important data, and necessary checks, for philosophy of science, including any theory of scientific method.

The history of science reveals, according to Kuhn, that scientific development occurs in alternating phases. During normal science, the members of the scientific community adhere to the paradigm in place. Their commitment to the paradigm means a commitment to the puzzles to be solved and the acceptable ways of solving them. Confidence in the paradigm remains so long as steady progress is made in solving the shared puzzles. Method in this normal phase operates within a disciplinary matrix (Kuhn’s later concept of a paradigm) which includes standards for problem solving, and defines the range of problems to which the method should be applied. An important part of a disciplinary matrix is the set of values which provide the norms and aims for scientific method. The main values that Kuhn identifies are prediction, problem solving, simplicity, consistency, and plausibility.

An important by-product of normal science is the accumulation of puzzles which cannot be solved with resources of the current paradigm. Once accumulation of these anomalies has reached some critical mass, it can trigger a communal shift to a new paradigm and a new phase of normal science. Importantly, the values that provide the norms and aims for scientific method may have transformed in the meantime. Method may therefore be relative to discipline, time or place

Feyerabend also identified the aims of science as progress, but argued that any methodological prescription would only stifle that progress (Feyerabend 1988). His arguments are grounded in re-examining accepted “myths” about the history of science. Heroes of science, like Galileo, are shown to be just as reliant on rhetoric and persuasion as they are on reason and demonstration. Others, like Aristotle, are shown to be far more reasonable and far-reaching in their outlooks then they are given credit for. As a consequence, the only rule that could provide what he took to be sufficient freedom was the vacuous “anything goes”. More generally, even the methodological restriction that science is the best way to pursue knowledge, and to increase knowledge, is too restrictive. Feyerabend suggested instead that science might, in fact, be a threat to a free society, because it and its myth had become so dominant (Feyerabend 1978).

An even more fundamental kind of criticism was offered by several sociologists of science from the 1970s onwards who rejected the methodology of providing philosophical accounts for the rational development of science and sociological accounts of the irrational mistakes. Instead, they adhered to a symmetry thesis on which any causal explanation of how scientific knowledge is established needs to be symmetrical in explaining truth and falsity, rationality and irrationality, success and mistakes, by the same causal factors (see, e.g., Barnes and Bloor 1982, Bloor 1991). Movements in the Sociology of Science, like the Strong Programme, or in the social dimensions and causes of knowledge more generally led to extended and close examination of detailed case studies in contemporary science and its history. (See the entries on the social dimensions of scientific knowledge and social epistemology .) Well-known examinations by Latour and Woolgar (1979/1986), Knorr-Cetina (1981), Pickering (1984), Shapin and Schaffer (1985) seem to bear out that it was social ideologies (on a macro-scale) or individual interactions and circumstances (on a micro-scale) which were the primary causal factors in determining which beliefs gained the status of scientific knowledge. As they saw it therefore, explanatory appeals to scientific method were not empirically grounded.

A late, and largely unexpected, criticism of scientific method came from within science itself. Beginning in the early 2000s, a number of scientists attempting to replicate the results of published experiments could not do so. There may be close conceptual connection between reproducibility and method. For example, if reproducibility means that the same scientific methods ought to produce the same result, and all scientific results ought to be reproducible, then whatever it takes to reproduce a scientific result ought to be called scientific method. Space limits us to the observation that, insofar as reproducibility is a desired outcome of proper scientific method, it is not strictly a part of scientific method. (See the entry on reproducibility of scientific results .)

By the close of the 20 th century the search for the scientific method was flagging. Nola and Sankey (2000b) could introduce their volume on method by remarking that “For some, the whole idea of a theory of scientific method is yester-year’s debate …”.

Despite the many difficulties that philosophers encountered in trying to providing a clear methodology of conformation (or refutation), still important progress has been made on understanding how observation can provide evidence for a given theory. Work in statistics has been crucial for understanding how theories can be tested empirically, and in recent decades a huge literature has developed that attempts to recast confirmation in Bayesian terms. Here these developments can be covered only briefly, and we refer to the entry on confirmation for further details and references.

Statistics has come to play an increasingly important role in the methodology of the experimental sciences from the 19 th century onwards. At that time, statistics and probability theory took on a methodological role as an analysis of inductive inference, and attempts to ground the rationality of induction in the axioms of probability theory have continued throughout the 20 th century and in to the present. Developments in the theory of statistics itself, meanwhile, have had a direct and immense influence on the experimental method, including methods for measuring the uncertainty of observations such as the Method of Least Squares developed by Legendre and Gauss in the early 19 th century, criteria for the rejection of outliers proposed by Peirce by the mid-19 th century, and the significance tests developed by Gosset (a.k.a. “Student”), Fisher, Neyman & Pearson and others in the 1920s and 1930s (see, e.g., Swijtink 1987 for a brief historical overview; and also the entry on C.S. Peirce ).

These developments within statistics then in turn led to a reflective discussion among both statisticians and philosophers of science on how to perceive the process of hypothesis testing: whether it was a rigorous statistical inference that could provide a numerical expression of the degree of confidence in the tested hypothesis, or if it should be seen as a decision between different courses of actions that also involved a value component. This led to a major controversy among Fisher on the one side and Neyman and Pearson on the other (see especially Fisher 1955, Neyman 1956 and Pearson 1955, and for analyses of the controversy, e.g., Howie 2002, Marks 2000, Lenhard 2006). On Fisher’s view, hypothesis testing was a methodology for when to accept or reject a statistical hypothesis, namely that a hypothesis should be rejected by evidence if this evidence would be unlikely relative to other possible outcomes, given the hypothesis were true. In contrast, on Neyman and Pearson’s view, the consequence of error also had to play a role when deciding between hypotheses. Introducing the distinction between the error of rejecting a true hypothesis (type I error) and accepting a false hypothesis (type II error), they argued that it depends on the consequences of the error to decide whether it is more important to avoid rejecting a true hypothesis or accepting a false one. Hence, Fisher aimed for a theory of inductive inference that enabled a numerical expression of confidence in a hypothesis. To him, the important point was the search for truth, not utility. In contrast, the Neyman-Pearson approach provided a strategy of inductive behaviour for deciding between different courses of action. Here, the important point was not whether a hypothesis was true, but whether one should act as if it was.

Similar discussions are found in the philosophical literature. On the one side, Churchman (1948) and Rudner (1953) argued that because scientific hypotheses can never be completely verified, a complete analysis of the methods of scientific inference includes ethical judgments in which the scientists must decide whether the evidence is sufficiently strong or that the probability is sufficiently high to warrant the acceptance of the hypothesis, which again will depend on the importance of making a mistake in accepting or rejecting the hypothesis. Others, such as Jeffrey (1956) and Levi (1960) disagreed and instead defended a value-neutral view of science on which scientists should bracket their attitudes, preferences, temperament, and values when assessing the correctness of their inferences. For more details on this value-free ideal in the philosophy of science and its historical development, see Douglas (2009) and Howard (2003). For a broad set of case studies examining the role of values in science, see e.g. Elliott & Richards 2017.

In recent decades, philosophical discussions of the evaluation of probabilistic hypotheses by statistical inference have largely focused on Bayesianism that understands probability as a measure of a person’s degree of belief in an event, given the available information, and frequentism that instead understands probability as a long-run frequency of a repeatable event. Hence, for Bayesians probabilities refer to a state of knowledge, whereas for frequentists probabilities refer to frequencies of events (see, e.g., Sober 2008, chapter 1 for a detailed introduction to Bayesianism and frequentism as well as to likelihoodism). Bayesianism aims at providing a quantifiable, algorithmic representation of belief revision, where belief revision is a function of prior beliefs (i.e., background knowledge) and incoming evidence. Bayesianism employs a rule based on Bayes’ theorem, a theorem of the probability calculus which relates conditional probabilities. The probability that a particular hypothesis is true is interpreted as a degree of belief, or credence, of the scientist. There will also be a probability and a degree of belief that a hypothesis will be true conditional on a piece of evidence (an observation, say) being true. Bayesianism proscribes that it is rational for the scientist to update their belief in the hypothesis to that conditional probability should it turn out that the evidence is, in fact, observed (see, e.g., Sprenger & Hartmann 2019 for a comprehensive treatment of Bayesian philosophy of science). Originating in the work of Neyman and Person, frequentism aims at providing the tools for reducing long-run error rates, such as the error-statistical approach developed by Mayo (1996) that focuses on how experimenters can avoid both type I and type II errors by building up a repertoire of procedures that detect errors if and only if they are present. Both Bayesianism and frequentism have developed over time, they are interpreted in different ways by its various proponents, and their relations to previous criticism to attempts at defining scientific method are seen differently by proponents and critics. The literature, surveys, reviews and criticism in this area are vast and the reader is referred to the entries on Bayesian epistemology and confirmation .

5. Method in Practice

Attention to scientific practice, as we have seen, is not itself new. However, the turn to practice in the philosophy of science of late can be seen as a correction to the pessimism with respect to method in philosophy of science in later parts of the 20 th century, and as an attempted reconciliation between sociological and rationalist explanations of scientific knowledge. Much of this work sees method as detailed and context specific problem-solving procedures, and methodological analyses to be at the same time descriptive, critical and advisory (see Nickles 1987 for an exposition of this view). The following section contains a survey of some of the practice focuses. In this section we turn fully to topics rather than chronology.

A problem with the distinction between the contexts of discovery and justification that figured so prominently in philosophy of science in the first half of the 20 th century (see section 2 ) is that no such distinction can be clearly seen in scientific activity (see Arabatzis 2006). Thus, in recent decades, it has been recognized that study of conceptual innovation and change should not be confined to psychology and sociology of science, but are also important aspects of scientific practice which philosophy of science should address (see also the entry on scientific discovery ). Looking for the practices that drive conceptual innovation has led philosophers to examine both the reasoning practices of scientists and the wide realm of experimental practices that are not directed narrowly at testing hypotheses, that is, exploratory experimentation.

Examining the reasoning practices of historical and contemporary scientists, Nersessian (2008) has argued that new scientific concepts are constructed as solutions to specific problems by systematic reasoning, and that of analogy, visual representation and thought-experimentation are among the important reasoning practices employed. These ubiquitous forms of reasoning are reliable—but also fallible—methods of conceptual development and change. On her account, model-based reasoning consists of cycles of construction, simulation, evaluation and adaption of models that serve as interim interpretations of the target problem to be solved. Often, this process will lead to modifications or extensions, and a new cycle of simulation and evaluation. However, Nersessian also emphasizes that

creative model-based reasoning cannot be applied as a simple recipe, is not always productive of solutions, and even its most exemplary usages can lead to incorrect solutions. (Nersessian 2008: 11)

Thus, while on the one hand she agrees with many previous philosophers that there is no logic of discovery, discoveries can derive from reasoned processes, such that a large and integral part of scientific practice is

the creation of concepts through which to comprehend, structure, and communicate about physical phenomena …. (Nersessian 1987: 11)

Similarly, work on heuristics for discovery and theory construction by scholars such as Darden (1991) and Bechtel & Richardson (1993) present science as problem solving and investigate scientific problem solving as a special case of problem-solving in general. Drawing largely on cases from the biological sciences, much of their focus has been on reasoning strategies for the generation, evaluation, and revision of mechanistic explanations of complex systems.

Addressing another aspect of the context distinction, namely the traditional view that the primary role of experiments is to test theoretical hypotheses according to the H-D model, other philosophers of science have argued for additional roles that experiments can play. The notion of exploratory experimentation was introduced to describe experiments driven by the desire to obtain empirical regularities and to develop concepts and classifications in which these regularities can be described (Steinle 1997, 2002; Burian 1997; Waters 2007)). However the difference between theory driven experimentation and exploratory experimentation should not be seen as a sharp distinction. Theory driven experiments are not always directed at testing hypothesis, but may also be directed at various kinds of fact-gathering, such as determining numerical parameters. Vice versa , exploratory experiments are usually informed by theory in various ways and are therefore not theory-free. Instead, in exploratory experiments phenomena are investigated without first limiting the possible outcomes of the experiment on the basis of extant theory about the phenomena.

The development of high throughput instrumentation in molecular biology and neighbouring fields has given rise to a special type of exploratory experimentation that collects and analyses very large amounts of data, and these new ‘omics’ disciplines are often said to represent a break with the ideal of hypothesis-driven science (Burian 2007; Elliott 2007; Waters 2007; O’Malley 2007) and instead described as data-driven research (Leonelli 2012; Strasser 2012) or as a special kind of “convenience experimentation” in which many experiments are done simply because they are extraordinarily convenient to perform (Krohs 2012).

5.2 Computer methods and ‘new ways’ of doing science

The field of omics just described is possible because of the ability of computers to process, in a reasonable amount of time, the huge quantities of data required. Computers allow for more elaborate experimentation (higher speed, better filtering, more variables, sophisticated coordination and control), but also, through modelling and simulations, might constitute a form of experimentation themselves. Here, too, we can pose a version of the general question of method versus practice: does the practice of using computers fundamentally change scientific method, or merely provide a more efficient means of implementing standard methods?

Because computers can be used to automate measurements, quantifications, calculations, and statistical analyses where, for practical reasons, these operations cannot be otherwise carried out, many of the steps involved in reaching a conclusion on the basis of an experiment are now made inside a “black box”, without the direct involvement or awareness of a human. This has epistemological implications, regarding what we can know, and how we can know it. To have confidence in the results, computer methods are therefore subjected to tests of verification and validation.

The distinction between verification and validation is easiest to characterize in the case of computer simulations. In a typical computer simulation scenario computers are used to numerically integrate differential equations for which no analytic solution is available. The equations are part of the model the scientist uses to represent a phenomenon or system under investigation. Verifying a computer simulation means checking that the equations of the model are being correctly approximated. Validating a simulation means checking that the equations of the model are adequate for the inferences one wants to make on the basis of that model.

A number of issues related to computer simulations have been raised. The identification of validity and verification as the testing methods has been criticized. Oreskes et al. (1994) raise concerns that “validiation”, because it suggests deductive inference, might lead to over-confidence in the results of simulations. The distinction itself is probably too clean, since actual practice in the testing of simulations mixes and moves back and forth between the two (Weissart 1997; Parker 2008a; Winsberg 2010). Computer simulations do seem to have a non-inductive character, given that the principles by which they operate are built in by the programmers, and any results of the simulation follow from those in-built principles in such a way that those results could, in principle, be deduced from the program code and its inputs. The status of simulations as experiments has therefore been examined (Kaufmann and Smarr 1993; Humphreys 1995; Hughes 1999; Norton and Suppe 2001). This literature considers the epistemology of these experiments: what we can learn by simulation, and also the kinds of justifications which can be given in applying that knowledge to the “real” world. (Mayo 1996; Parker 2008b). As pointed out, part of the advantage of computer simulation derives from the fact that huge numbers of calculations can be carried out without requiring direct observation by the experimenter/​simulator. At the same time, many of these calculations are approximations to the calculations which would be performed first-hand in an ideal situation. Both factors introduce uncertainties into the inferences drawn from what is observed in the simulation.

For many of the reasons described above, computer simulations do not seem to belong clearly to either the experimental or theoretical domain. Rather, they seem to crucially involve aspects of both. This has led some authors, such as Fox Keller (2003: 200) to argue that we ought to consider computer simulation a “qualitatively different way of doing science”. The literature in general tends to follow Kaufmann and Smarr (1993) in referring to computer simulation as a “third way” for scientific methodology (theoretical reasoning and experimental practice are the first two ways.). It should also be noted that the debates around these issues have tended to focus on the form of computer simulation typical in the physical sciences, where models are based on dynamical equations. Other forms of simulation might not have the same problems, or have problems of their own (see the entry on computer simulations in science ).

In recent years, the rapid development of machine learning techniques has prompted some scholars to suggest that the scientific method has become “obsolete” (Anderson 2008, Carrol and Goodstein 2009). This has resulted in an intense debate on the relative merit of data-driven and hypothesis-driven research (for samples, see e.g. Mazzocchi 2015 or Succi and Coveney 2018). For a detailed treatment of this topic, we refer to the entry scientific research and big data .

6. Discourse on scientific method

Despite philosophical disagreements, the idea of the scientific method still figures prominently in contemporary discourse on many different topics, both within science and in society at large. Often, reference to scientific method is used in ways that convey either the legend of a single, universal method characteristic of all science, or grants to a particular method or set of methods privilege as a special ‘gold standard’, often with reference to particular philosophers to vindicate the claims. Discourse on scientific method also typically arises when there is a need to distinguish between science and other activities, or for justifying the special status conveyed to science. In these areas, the philosophical attempts at identifying a set of methods characteristic for scientific endeavors are closely related to the philosophy of science’s classical problem of demarcation (see the entry on science and pseudo-science ) and to the philosophical analysis of the social dimension of scientific knowledge and the role of science in democratic society.

One of the settings in which the legend of a single, universal scientific method has been particularly strong is science education (see, e.g., Bauer 1992; McComas 1996; Wivagg & Allchin 2002). [ 5 ] Often, ‘the scientific method’ is presented in textbooks and educational web pages as a fixed four or five step procedure starting from observations and description of a phenomenon and progressing over formulation of a hypothesis which explains the phenomenon, designing and conducting experiments to test the hypothesis, analyzing the results, and ending with drawing a conclusion. Such references to a universal scientific method can be found in educational material at all levels of science education (Blachowicz 2009), and numerous studies have shown that the idea of a general and universal scientific method often form part of both students’ and teachers’ conception of science (see, e.g., Aikenhead 1987; Osborne et al. 2003). In response, it has been argued that science education need to focus more on teaching about the nature of science, although views have differed on whether this is best done through student-led investigations, contemporary cases, or historical cases (Allchin, Andersen & Nielsen 2014)

Although occasionally phrased with reference to the H-D method, important historical roots of the legend in science education of a single, universal scientific method are the American philosopher and psychologist Dewey’s account of inquiry in How We Think (1910) and the British mathematician Karl Pearson’s account of science in Grammar of Science (1892). On Dewey’s account, inquiry is divided into the five steps of

(i) a felt difficulty, (ii) its location and definition, (iii) suggestion of a possible solution, (iv) development by reasoning of the bearing of the suggestions, (v) further observation and experiment leading to its acceptance or rejection. (Dewey 1910: 72)

Similarly, on Pearson’s account, scientific investigations start with measurement of data and observation of their correction and sequence from which scientific laws can be discovered with the aid of creative imagination. These laws have to be subject to criticism, and their final acceptance will have equal validity for “all normally constituted minds”. Both Dewey’s and Pearson’s accounts should be seen as generalized abstractions of inquiry and not restricted to the realm of science—although both Dewey and Pearson referred to their respective accounts as ‘the scientific method’.

Occasionally, scientists make sweeping statements about a simple and distinct scientific method, as exemplified by Feynman’s simplified version of a conjectures and refutations method presented, for example, in the last of his 1964 Cornell Messenger lectures. [ 6 ] However, just as often scientists have come to the same conclusion as recent philosophy of science that there is not any unique, easily described scientific method. For example, the physicist and Nobel Laureate Weinberg described in the paper “The Methods of Science … And Those By Which We Live” (1995) how

The fact that the standards of scientific success shift with time does not only make the philosophy of science difficult; it also raises problems for the public understanding of science. We do not have a fixed scientific method to rally around and defend. (1995: 8)

Interview studies with scientists on their conception of method shows that scientists often find it hard to figure out whether available evidence confirms their hypothesis, and that there are no direct translations between general ideas about method and specific strategies to guide how research is conducted (Schickore & Hangel 2019, Hangel & Schickore 2017)

Reference to the scientific method has also often been used to argue for the scientific nature or special status of a particular activity. Philosophical positions that argue for a simple and unique scientific method as a criterion of demarcation, such as Popperian falsification, have often attracted practitioners who felt that they had a need to defend their domain of practice. For example, references to conjectures and refutation as the scientific method are abundant in much of the literature on complementary and alternative medicine (CAM)—alongside the competing position that CAM, as an alternative to conventional biomedicine, needs to develop its own methodology different from that of science.

Also within mainstream science, reference to the scientific method is used in arguments regarding the internal hierarchy of disciplines and domains. A frequently seen argument is that research based on the H-D method is superior to research based on induction from observations because in deductive inferences the conclusion follows necessarily from the premises. (See, e.g., Parascandola 1998 for an analysis of how this argument has been made to downgrade epidemiology compared to the laboratory sciences.) Similarly, based on an examination of the practices of major funding institutions such as the National Institutes of Health (NIH), the National Science Foundation (NSF) and the Biomedical Sciences Research Practices (BBSRC) in the UK, O’Malley et al. (2009) have argued that funding agencies seem to have a tendency to adhere to the view that the primary activity of science is to test hypotheses, while descriptive and exploratory research is seen as merely preparatory activities that are valuable only insofar as they fuel hypothesis-driven research.

In some areas of science, scholarly publications are structured in a way that may convey the impression of a neat and linear process of inquiry from stating a question, devising the methods by which to answer it, collecting the data, to drawing a conclusion from the analysis of data. For example, the codified format of publications in most biomedical journals known as the IMRAD format (Introduction, Method, Results, Analysis, Discussion) is explicitly described by the journal editors as “not an arbitrary publication format but rather a direct reflection of the process of scientific discovery” (see the so-called “Vancouver Recommendations”, ICMJE 2013: 11). However, scientific publications do not in general reflect the process by which the reported scientific results were produced. For example, under the provocative title “Is the scientific paper a fraud?”, Medawar argued that scientific papers generally misrepresent how the results have been produced (Medawar 1963/1996). Similar views have been advanced by philosophers, historians and sociologists of science (Gilbert 1976; Holmes 1987; Knorr-Cetina 1981; Schickore 2008; Suppe 1998) who have argued that scientists’ experimental practices are messy and often do not follow any recognizable pattern. Publications of research results, they argue, are retrospective reconstructions of these activities that often do not preserve the temporal order or the logic of these activities, but are instead often constructed in order to screen off potential criticism (see Schickore 2008 for a review of this work).

Philosophical positions on the scientific method have also made it into the court room, especially in the US where judges have drawn on philosophy of science in deciding when to confer special status to scientific expert testimony. A key case is Daubert vs Merrell Dow Pharmaceuticals (92–102, 509 U.S. 579, 1993). In this case, the Supreme Court argued in its 1993 ruling that trial judges must ensure that expert testimony is reliable, and that in doing this the court must look at the expert’s methodology to determine whether the proffered evidence is actually scientific knowledge. Further, referring to works of Popper and Hempel the court stated that

ordinarily, a key question to be answered in determining whether a theory or technique is scientific knowledge … is whether it can be (and has been) tested. (Justice Blackmun, Daubert v. Merrell Dow Pharmaceuticals; see Other Internet Resources for a link to the opinion)

But as argued by Haack (2005a,b, 2010) and by Foster & Hubner (1999), by equating the question of whether a piece of testimony is reliable with the question whether it is scientific as indicated by a special methodology, the court was producing an inconsistent mixture of Popper’s and Hempel’s philosophies, and this has later led to considerable confusion in subsequent case rulings that drew on the Daubert case (see Haack 2010 for a detailed exposition).

The difficulties around identifying the methods of science are also reflected in the difficulties of identifying scientific misconduct in the form of improper application of the method or methods of science. One of the first and most influential attempts at defining misconduct in science was the US definition from 1989 that defined misconduct as

fabrication, falsification, plagiarism, or other practices that seriously deviate from those that are commonly accepted within the scientific community . (Code of Federal Regulations, part 50, subpart A., August 8, 1989, italics added)

However, the “other practices that seriously deviate” clause was heavily criticized because it could be used to suppress creative or novel science. For example, the National Academy of Science stated in their report Responsible Science (1992) that it

wishes to discourage the possibility that a misconduct complaint could be lodged against scientists based solely on their use of novel or unorthodox research methods. (NAS: 27)

This clause was therefore later removed from the definition. For an entry into the key philosophical literature on conduct in science, see Shamoo & Resnick (2009).

The question of the source of the success of science has been at the core of philosophy since the beginning of modern science. If viewed as a matter of epistemology more generally, scientific method is a part of the entire history of philosophy. Over that time, science and whatever methods its practitioners may employ have changed dramatically. Today, many philosophers have taken up the banners of pluralism or of practice to focus on what are, in effect, fine-grained and contextually limited examinations of scientific method. Others hope to shift perspectives in order to provide a renewed general account of what characterizes the activity we call science.

One such perspective has been offered recently by Hoyningen-Huene (2008, 2013), who argues from the history of philosophy of science that after three lengthy phases of characterizing science by its method, we are now in a phase where the belief in the existence of a positive scientific method has eroded and what has been left to characterize science is only its fallibility. First was a phase from Plato and Aristotle up until the 17 th century where the specificity of scientific knowledge was seen in its absolute certainty established by proof from evident axioms; next was a phase up to the mid-19 th century in which the means to establish the certainty of scientific knowledge had been generalized to include inductive procedures as well. In the third phase, which lasted until the last decades of the 20 th century, it was recognized that empirical knowledge was fallible, but it was still granted a special status due to its distinctive mode of production. But now in the fourth phase, according to Hoyningen-Huene, historical and philosophical studies have shown how “scientific methods with the characteristics as posited in the second and third phase do not exist” (2008: 168) and there is no longer any consensus among philosophers and historians of science about the nature of science. For Hoyningen-Huene, this is too negative a stance, and he therefore urges the question about the nature of science anew. His own answer to this question is that “scientific knowledge differs from other kinds of knowledge, especially everyday knowledge, primarily by being more systematic” (Hoyningen-Huene 2013: 14). Systematicity can have several different dimensions: among them are more systematic descriptions, explanations, predictions, defense of knowledge claims, epistemic connectedness, ideal of completeness, knowledge generation, representation of knowledge and critical discourse. Hence, what characterizes science is the greater care in excluding possible alternative explanations, the more detailed elaboration with respect to data on which predictions are based, the greater care in detecting and eliminating sources of error, the more articulate connections to other pieces of knowledge, etc. On this position, what characterizes science is not that the methods employed are unique to science, but that the methods are more carefully employed.

Another, similar approach has been offered by Haack (2003). She sets off, similar to Hoyningen-Huene, from a dissatisfaction with the recent clash between what she calls Old Deferentialism and New Cynicism. The Old Deferentialist position is that science progressed inductively by accumulating true theories confirmed by empirical evidence or deductively by testing conjectures against basic statements; while the New Cynics position is that science has no epistemic authority and no uniquely rational method and is merely just politics. Haack insists that contrary to the views of the New Cynics, there are objective epistemic standards, and there is something epistemologically special about science, even though the Old Deferentialists pictured this in a wrong way. Instead, she offers a new Critical Commonsensist account on which standards of good, strong, supportive evidence and well-conducted, honest, thorough and imaginative inquiry are not exclusive to the sciences, but the standards by which we judge all inquirers. In this sense, science does not differ in kind from other kinds of inquiry, but it may differ in the degree to which it requires broad and detailed background knowledge and a familiarity with a technical vocabulary that only specialists may possess.

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• Blackmun opinion , in Daubert v. Merrell Dow Pharmaceuticals (92–102), 509 U.S. 579 (1993).
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2.1: The Scientific Method

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Hypothesis Testing and The scientific Method

The scientific method is a process of research with defined steps that include data collection and careful observation. The scientific method was used even in ancient times, but it was first documented by England’s Sir Francis Bacon (1561–1626) (Figure \(\PageIndex{5}\)), who set up inductive methods for scientific inquiry.

Observation

Scientific advances begin with observations . This involves noticing a pattern, either directly or indirectly from the literature. An example of a direct observation is noticing that there have been a lot of toads in your yard ever since you turned on the sprinklers, where as an indirect observation would be reading a scientific study reporting high densities of toads in urban areas with watered lawns.

During the Vietnam War (figure \(\PageIndex{6}\)), press reports from North Vietnam documented an increasing rate of birth defects. While this credibility of this information was initially questioned by the U.S., it evoked questions about what could be causing these birth defects. Furthermore, increased incidence of certain cancers and other diseases later emerged in Vietnam veterans who had returned to the U.S. This leads us to the next step of the scientific method, the question.

Figure \(\PageIndex{6}\): A map of Vietnam 1954-1975. Image from Bureau of Public Affairs U.S. Government Printing Office (public domain).

The question step of the scientific method is simply asking, what explains the observed pattern? Multiple questions can stem from a single observation. Scientists and the public began to ask, what is causing the birth defects in Vietnam and diseases in Vietnam veterans? Could it be associated with the widespread military use of the herbicide Agent Orange to clear the forests (figure \(\PageIndex{7-8}\)), which helped identify enemies more easily?

Figure \(\PageIndex{7}\): Agent Orange drums in Vietnam. Image by U.S. Government (public domain).

Figure \(\PageIndex{8}\): A healthy mangrove forest (top), and another forest after application of Agent Orange. Image by unknown author (public domain).

Hypothesis and Prediction

The hypothesis is the expected answer to the question. The best hypotheses state the proposed direction of the effect (increases, decreases, etc.) and explain why the hypothesis could be true.

• OK hypothesis: Agent Orange influences rates of birth defects and disease.
• Better hypothesis: Agent Orange increases the incidence of birth defects and disease.
• Best hypothesis: Agent Orange increases the incidence of birth defects and disease because these health problems have been frequently reported by individuals exposed to this herbicide.

If two or more hypotheses meet this standard, the simpler one is preferred.

Predictions stem from the hypothesis. The prediction explains what results would support hypothesis. The prediction is more specific than the hypothesis because it references the details of the experiment. For example, "If Agent Orange causes health problems, then mice experimentally exposed to TCDD, a contaminant of Agent Orange, during development will have more frequent birth defects than control mice" (figure \(\PageIndex{9}\)).

Figure \(\PageIndex{9}\): The chemical structure of TCDD (2,3,7,8-tetrachlorodibenzo-p-dioxin), which is produced when synthesizing the chemicals in Agent Orange. It contaminates Agent Orange at low but harmful concentrations. Image by Emeldir (public domain).

Hypotheses and predictions must be testable to ensure that it is valid. For example, a hypothesis that depends on what a bear thinks is not testable, because it can never be known what a bear thinks. It should also be falsifiable , meaning that they have the capacity to be tested and demonstrated to be untrue. An example of an unfalsifiable hypothesis is “Botticelli’s Birth of Venus is beautiful.” There is no experiment that might show this statement to be false. To test a hypothesis, a researcher will conduct one or more experiments designed to eliminate one or more of the hypotheses. This is important. A hypothesis can be disproven, or eliminated, but it can never be proven. Science does not deal in proofs like mathematics. If an experiment fails to disprove a hypothesis, then we find support for that explanation, but this is not to say that down the road a better explanation will not be found, or a more carefully designed experiment will be found to falsify the hypothesis.

Hypotheses are tentative explanations and are different from scientific theories. A scientific theory is a widely-accepted, thoroughly tested and confirmed explanation for a set of observations or phenomena. Scientific theory is the foundation of scientific knowledge. In addition, in many scientific disciplines (less so in biology) there are scientific laws , often expressed in mathematical formulas, which describe how elements of nature will behave under certain specific conditions, but they do not offer explanations for why they occur.

Design an Experiment

Next, a scientific study (experiment) is planned to test the hypothesis and determine whether the results match the predictions. Each experiment will have one or more variables. The explanatory variable is what scientists hypothesize might be causing something else. In a manipulative experiment (see below), the explanatory variable is manipulated by the scientist. The response variable is the response, the variable ultimately measured in the study. Controlled variables (confounding factors) might affect the response variable, but they are not the focus of the study. Scientist attempt to standardize the controlled variables so that they do not influence the results. In our previous example, exposure to Agent Orange is the explanatory variable. It is hypothesized to cause a change in health (likelihood of having children with birth defects or developing a disease), the response variable. Many other things could affect health, including diet, exercise, and family history. These are the controlled variables.

There are two main types of scientific studies: experimental studies (manipulative experiments) and observational studies.

In a manipulative experiment , the explanatory variable is altered by the scientists, who then observe the response. In other words, the scientists apply a treatment . An example would be exposing developing mice to TCDD and comparing the rate of birth defects to a control group. The control group is group of test subjects that are as similar as possible to all other test subjects, with the exception that they don’t receive the experimental treatment (those that do receive it are known as the experimental, treatment, or test group ). The purpose of the control group is to establish what the dependent variable would be under normal conditions, in the absence of the experimental treatment. It serves as a baseline to which the test group can be compared. In this example, the control group would contain mice that were not exposed to TCDD but were otherwise handled the same way as the other mice (figure \(\PageIndex{10}\))

Figure \(\PageIndex{10}\): Laboratory mice. In a proper scientific study, the treatment would be applied to multiple mice. Another group of mice would not receive the treatment (the control group). Image by Aaron Logan ( CC-BY ).

In an observational study , scientists examine multiple samples with and without the presumed cause. An example would be monitoring the health of veterans who had varying levels of exposure to Agent Orange.

Scientific studies contain many replicates. Multiple samples ensure that any observed pattern is due to the treatment rather than naturally occurring differences between individuals. A scientific study should also be repeatable , meaning that if it is conducted again, following the same procedure, it should reproduce the same general results. Additionally, multiple studies will ultimately test the same hypothesis.

Finally, the data are collected and the results are analyzed. As described in the Math Blast chapter, statistics can be used to describe the data and summarize data. They also provide a criterion for deciding whether the pattern in the data is strong enough to support the hypothesis.

The manipulative experiment in our example found that mice exposed to high levels of 2,4,5-T (a component of Agent Orange) or TCDD (a contaminant found in Agent Orange) during development had a cleft palate birth defect more frequently than control mice (figure \(\PageIndex{11}\)). Mice embryos were also more likely to die when exposed to TCDD compared to controls.

Figure \(\PageIndex{11}\): Cleft lip and palate, a birth defect in which these structures are split. Image by James Heilman, MD ( CC-BY-SA ).

An observational study found that self-reported exposure to Agent Orange was positively correlated with incidence of multiple diseases in Korean veterans of the Vietnam War, including various cancers, diseases of the cardiovascular and nervous systems, skin diseases, and psychological disorders. Note that a positive correlation simply means that the independent and dependent variables both increase or decrease together, but further data, such as the evidence provided by manipulative experiments is needed to document a cause-and-effect relationship . (A negative correlation occurs when one variable increases as the other decreases.)

Lastly, scientists make a conclusion regarding whether the data support the hypothesis. In the case of Agent Orange, the data, that mice exposed to TCDD and 2,4,5-T had higher frequencies of cleft palate, matches the prediction. Additionally, veterans exposed to Agent Orange had higher rates of certain diseases, further supporting the hypothesis. We can thus accept the hypothesis that Agent Orange increases the incidence of birth defects and disease.

Scientific Method in Practice

In practice, the scientific method is not as rigid and structured as it might first appear. Sometimes an experiment leads to conclusions that favor a change in approach; often, an experiment brings entirely new scientific questions to the puzzle. Many times, science does not operate in a linear fashion; instead, scientists continually draw inferences and make generalizations, finding patterns as their research proceeds (figure \(\PageIndex{12}\)). Even if the hypothesis was supported, scientists may still continue to test it in different ways. For example, scientists explore the impacts of Agent Orange, examining long-term health impacts as Vietnam veterans age.

Scientific findings can influence decision making. In response to evidence regarding the effect of Agent Orange on human health, compensation is now available for Vietnam veterans who were exposed to Agent Orange and develop certain diseases. The use of Agent Orange is also banned in the U.S. Finally, the U.S. has began cleaning sites in Vietnam that are still contaminated with TCDD.

As another simple example, an experiment might be conducted to test the hypothesis that phosphate limits the growth of algae in freshwater ponds. A series of artificial ponds are filled with water and half of them are treated by adding phosphate each week, while the other half are treated by adding a salt that is known not to be used by algae. The variable here is the phosphate (or lack of phosphate), the experimental or treatment cases are the ponds with added phosphate and the control ponds are those with something inert added, such as the salt. Just adding something is also a control against the possibility that adding extra matter to the pond has an effect. If the treated ponds show lesser growth of algae, then we have found support for our hypothesis. If they do not, then we reject our hypothesis. Be aware that rejecting one hypothesis does not determine whether or not the other hypotheses can be accepted; it simply eliminates one hypothesis that is not valid (Figure \(\PageIndex{12}\)). Using the scientific method, the hypotheses that are inconsistent with experimental data are rejected.

Institute of Medicine (US) Committee to Review the Health Effects in Vietnam Veterans of Exposure to Herbicides. Veterans and Agent Orange: Health Effects of Herbicides Used in Vietnam . Washington (DC): National Academies Press (US); 1994. 2, History of the Controversy Over the Use of Herbicides.

Neubert, D., Dillmann, I. Embryotoxic effects in mice treated with 2,4,5-trichlorophenoxyacetic acid and 2,3,7,8-tetrachlorodibenzo-p-dioxin . Naunyn-Schmiedeberg's Arch. Pharmacol. 272, 243–264 (1972).

Stellman, J. M., & Stellman, S. D. (2018). Agent Orange During the Vietnam War: The Lingering Issue of Its Civilian and Military Health Impact . American journal of public health , 108 (6), 726–728.

Yi, S. W., Ohrr, H., Hong, J. S., & Yi, J. J. (2013). Agent Orange exposure and prevalence of self-reported diseases in Korean Vietnam veterans . Journal of preventive medicine and public health = Yebang Uihakhoe chi , 46 (5), 213–225.

American Association for the Advancement of Science (AAAS). 1990. Science for All Americans. AAAS, Washington, DC.

Barnes, B. 1985. About Science. Blackwell Ltd ,London, UK.

Giere, R.N. 2005. Understanding Scientific Reasoning. 5th ed. Wadsworth Publishing, New York, NY.

Kuhn, T.S. 1996. The Structure of Scientific Revolutions. 3rd ed. University of Chicago Press, Chicago, IL.

McCain, G. and E.M. Siegal. 1982. The Game of Science. Holbrook Press Inc., Boston, MA.

Moore, J.A. 1999. Science as a Way of Knowing. Harvard University Press, Boston, MA.

Popper, K. 1979. Objective Knowledge: An Evolutionary Approach. Clarendon Press, Oxford, UK.

Raven, P.H., G.B. Johnson, K.A. Mason, and J. Losos. 2013. Biology. 10th ed. McGraw-Hill, Columbus, OH.

Silver, B.L. 2000. The Ascent of Science. Oxford University Press, Oxford, UK.

Contributors and Attributions

• Modified by Kyle Whittinghill (University of Pittsburgh)

Samantha Fowler (Clayton State University), Rebecca Roush (Sandhills Community College), James Wise (Hampton University). Original content by OpenStax (CC BY 4.0; Access for free at https://cnx.org/contents/b3c1e1d2-83...4-e119a8aafbdd ).

• Modified by Melissa Ha
• 1.2: The Process of Science by OpenStax , is licensed CC BY
• What is Science? from An Introduction to Geology by Chris Johnson et al. (licensed under CC-BY-NC-SA )
• The Process of Science from Environmental Biology by Matthew R. Fisher (licensed under CC-BY )
• Scientific Methods from Biology by John W. Kimball (licensed under CC-BY )
• Scientific Papers from Biology by John W. Kimball ( CC-BY )
• Environmental Science: A Canadian perspective by Bill Freedman Chapter 2: Science as a Way of Understanding the Natural World

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Perspective

Perspective: Dimensions of the scientific method

* E-mail: [email protected]

Affiliation Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, United States of America

• Eberhard O. Voit

Published: September 12, 2019

• https://doi.org/10.1371/journal.pcbi.1007279
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The scientific method has been guiding biological research for a long time. It not only prescribes the order and types of activities that give a scientific study validity and a stamp of approval but also has substantially shaped how we collectively think about the endeavor of investigating nature. The advent of high-throughput data generation, data mining, and advanced computational modeling has thrown the formerly undisputed, monolithic status of the scientific method into turmoil. On the one hand, the new approaches are clearly successful and expect the same acceptance as the traditional methods, but on the other hand, they replace much of the hypothesis-driven reasoning with inductive argumentation, which philosophers of science consider problematic. Intrigued by the enormous wealth of data and the power of machine learning, some scientists have even argued that significant correlations within datasets could make the entire quest for causation obsolete. Many of these issues have been passionately debated during the past two decades, often with scant agreement. It is proffered here that hypothesis-driven, data-mining–inspired, and “allochthonous” knowledge acquisition, based on mathematical and computational models, are vectors spanning a 3D space of an expanded scientific method. The combination of methods within this space will most certainly shape our thinking about nature, with implications for experimental design, peer review and funding, sharing of result, education, medical diagnostics, and even questions of litigation.

Citation: Voit EO (2019) Perspective: Dimensions of the scientific method. PLoS Comput Biol 15(9): e1007279. https://doi.org/10.1371/journal.pcbi.1007279

Editor: Jason A. Papin, University of Virginia, UNITED STATES

Copyright: © 2019 Eberhard O. Voit. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This work was supported in part by grants from the National Science Foundation ( https://www.nsf.gov/div/index.jsp?div=MCB ) grant NSF-MCB-1517588 (PI: EOV), NSF-MCB-1615373 (PI: Diana Downs) and the National Institute of Environmental Health Sciences ( https://www.niehs.nih.gov/ ) grant NIH-2P30ES019776-05 (PI: Carmen Marsit). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The author has declared that no competing interests exist.

The traditional scientific method: Hypothesis-driven deduction

Research is the undisputed core activity defining science. Without research, the advancement of scientific knowledge would come to a screeching halt. While it is evident that researchers look for new information or insights, the term “research” is somewhat puzzling. Never mind the prefix “re,” which simply means “coming back and doing it again and again,” the word “search” seems to suggest that the research process is somewhat haphazard, that not much of a strategy is involved in the process. One might argue that research a few hundred years ago had the character of hoping for enough luck to find something new. The alchemists come to mind in their quest to turn mercury or lead into gold, or to discover an elixir for eternal youth, through methods we nowadays consider laughable.

Today’s sciences, in stark contrast, are clearly different. Yes, we still try to find something new—and may need a good dose of luck—but the process is anything but unstructured. In fact, it is prescribed in such rigor that it has been given the widely known moniker “scientific method.” This scientific method has deep roots going back to Aristotle and Herophilus (approximately 300 BC), Avicenna and Alhazen (approximately 1,000 AD), Grosseteste and Robert Bacon (approximately 1,250 AD), and many others, but solidified and crystallized into the gold standard of quality research during the 17th and 18th centuries [ 1 – 7 ]. In particular, Sir Francis Bacon (1561–1626) and René Descartes (1596–1650) are often considered the founders of the scientific method, because they insisted on careful, systematic observations of high quality, rather than metaphysical speculations that were en vogue among the scholars of the time [ 1 , 8 ]. In contrast to their peers, they strove for objectivity and insisted that observations, rather than an investigator’s preconceived ideas or superstitions, should be the basis for formulating a research idea [ 7 , 9 ].

Bacon and his 19th century follower John Stuart Mill explicitly proposed gaining knowledge through inductive reasoning: Based on carefully recorded observations, or from data obtained in a well-planned experiment, generalized assertions were to be made about similar yet (so far) unobserved phenomena [ 7 ]. Expressed differently, inductive reasoning attempts to derive general principles or laws directly from empirical evidence [ 10 ]. An example is the 19th century epigram of the physician Rudolf Virchow, Omnis cellula e cellula . There is no proof that indeed “every cell derives from a cell,” but like Virchow, we have made the observation time and again and never encountered anything suggesting otherwise.

In contrast to induction, the widely accepted, traditional scientific method is based on formulating and testing hypotheses. From the results of these tests, a deduction is made whether the hypothesis is presumably true or false. This type of hypotheticodeductive reasoning goes back to William Whewell, William Stanley Jevons, and Charles Peirce in the 19th century [ 1 ]. By the 20th century, the deductive, hypothesis-based scientific method had become deeply ingrained in the scientific psyche, and it is now taught as early as middle school in order to teach students valid means of discovery [ 8 , 11 , 12 ]. The scientific method has not only guided most research studies but also fundamentally influenced how we think about the process of scientific discovery.

Alas, because biology has almost no general laws, deduction in the strictest sense is difficult. It may therefore be preferable to use the term abduction, which refers to the logical inference toward the most plausible explanation, given a set of observations, although this explanation cannot be proven and is not necessarily true.

Over the decades, the hypothesis-based scientific method did experience variations here and there, but its conceptual scaffold remained essentially unchanged ( Fig 1 ). Its key is a process that begins with the formulation of a hypothesis that is to be rigorously tested, either in the wet lab or computationally; nonadherence to this principle is seen as lacking rigor and can lead to irreproducible results [ 1 , 13 – 15 ].

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The central concept of the traditional scientific method is a falsifiable hypothesis regarding some phenomenon of interest. This hypothesis is to be tested experimentally or computationally. The test results support or refute the hypothesis, triggering a new round of hypothesis formulation and testing.

https://doi.org/10.1371/journal.pcbi.1007279.g001

Going further, the prominent philosopher of science Sir Karl Popper argued that a scientific hypothesis can never be verified but that it can be disproved by a single counterexample. He therefore demanded that scientific hypotheses had to be falsifiable, because otherwise, testing would be moot [ 16 , 17 ] (see also [ 18 ]). As Gillies put it, “successful theories are those that survive elimination through falsification” [ 19 ]. Kelley and Scott agreed to some degree but warned that complete insistence on falsifiability is too restrictive as it would mark many computational techniques, statistical hypothesis testing, and even Darwin’s theory of evolution as nonscientific [ 20 ].

While the hypothesis-based scientific method has been very successful, its exclusive reliance on deductive reasoning is dangerous because according to the so-called Duhem–Quine thesis, hypothesis testing always involves an unknown number of explicit or implicit assumptions, some of which may steer the researcher away from hypotheses that seem implausible, although they are, in fact, true [ 21 ]. According to Kuhn, this bias can obstruct the recognition of paradigm shifts [ 22 ], which require the rethinking of previously accepted “truths” and the development of radically new ideas [ 23 , 24 ]. The testing of simultaneous alternative hypotheses [ 25 – 27 ] ameliorates this problem to some degree but not entirely.

The traditional scientific method is often presented in discrete steps, but it should really be seen as a form of critical thinking, subject to review and independent validation [ 8 ]. It has proven very influential, not only by prescribing valid experimentation, but also for affecting the way we attempt to understand nature [ 18 ], for teaching [ 8 , 12 ], reporting, publishing, and otherwise sharing information [ 28 ], for peer review and the awarding of funds by research-supporting agencies [ 29 , 30 ], for medical diagnostics [ 7 ], and even in litigation [ 31 ].

A second dimension of the scientific method: Data-mining–inspired induction

A major shift in biological experimentation occurred with the–omics revolution of the early 21st century. All of a sudden, it became feasible to perform high-throughput experiments that generated thousands of measurements, typically characterizing the expression or abundances of very many—if not all—genes, proteins, metabolites, or other biological quantities in a sample.

The strategy of measuring large numbers of items in a nontargeted fashion is fundamentally different from the traditional scientific method and constitutes a new, second dimension of the scientific method. Instead of hypothesizing and testing whether gene X is up-regulated under some altered condition, the leading question becomes which of the thousands of genes in a sample are up- or down-regulated. This shift in focus elevates the data to the supreme role of revealing novel insights by themselves ( Fig 2 ). As an important, generic advantage over the traditional strategy, this second dimension is free of a researcher’s preconceived notions regarding the molecular mechanisms governing the phenomenon of interest, which are otherwise the key to formulating a hypothesis. The prominent biologists Patrick Brown and David Botstein commented that “the patterns of expression will often suffice to begin de novo discovery of potential gene functions” [ 32 ].

Data-driven research begins with an untargeted exploration, in which the data speak for themselves. Machine learning extracts patterns from the data, which suggest hypotheses that are to be tested in the lab or computationally.

https://doi.org/10.1371/journal.pcbi.1007279.g002

This data-driven, discovery-generating approach is at once appealing and challenging. On the one hand, very many data are explored simultaneously and essentially without bias. On the other hand, the large datasets supporting this approach create a genuine challenge to understanding and interpreting the experimental results because the thousands of data points, often superimposed with a fair amount of noise, make it difficult to detect meaningful differences between sample and control. This situation can only be addressed with computational methods that first “clean” the data, for instance, through the statistically valid removal of outliers, and then use machine learning to identify statistically significant, distinguishing molecular profiles or signatures. In favorable cases, such signatures point to specific biological pathways, whereas other signatures defy direct explanation but may become the launch pad for follow-up investigations [ 33 ].

Today’s scientists are very familiar with this discovery-driven exploration of “what’s out there” and might consider it a quaint quirk of history that this strategy was at first widely chastised and ridiculed as a “fishing expedition” [ 30 , 34 ]. Strict traditionalists were outraged that rigor was leaving science with the new approach and that sufficient guidelines were unavailable to assure the validity and reproducibility of results [ 10 , 35 , 36 ].

From the view point of philosophy of science, this second dimension of the scientific method uses inductive reasoning and reflects Bacon’s idea that observations can and should dictate the research question to be investigated [ 1 , 7 ]. Allen [ 36 ] forcefully rejected this type of reasoning, stating “the thinking goes, we can now expect computer programs to derive significance, relevance and meaning from chunks of information, be they nucleotide sequences or gene expression profiles… In contrast with this view, many are convinced that no purely logical process can turn observation into understanding.” His conviction goes back to the 18th century philosopher David Hume and again to Popper, who identified as the overriding problem with inductive reasoning that it can never truly reveal causality, even if a phenomenon is observed time and again [ 16 , 17 , 37 , 38 ]. No number of observations, even if they always have the same result, can guard against an exception that would violate the generality of a law inferred from these observations [ 1 , 35 ]. Worse, Popper argued, through inference by induction, we cannot even know the probability of something being true [ 10 , 17 , 36 ].

Others argued that data-driven and hypothesis-driven research actually do not differ all that much in principle, as long as there is cycling between developing new ideas and testing them with care [ 27 ]. In fact, Kell and Oliver [ 34 ] maintained that the exclusive acceptance of hypothesis-driven programs misrepresents the complexities of biological knowledge generation. Similarly refuting the prominent rule of deduction, Platt [ 26 ] and Beard and Kushmerick [ 27 ] argued that repeated inductive reasoning, called strong inference, corresponds to a logically sound decision tree of disproving or refining hypotheses that can rapidly yield firm conclusions; nonetheless, Platt had to admit that inductive inference is not as certain as deduction, because it projects into the unknown. Lander compared the task of obtaining causality by induction to the problem of inferring the design of a microprocessor from input-output readings, which in a strict sense is impossible, because the microprocessor could be arbitrarily complicated; even so, inference often leads to novel insights and therefore is valuable [ 39 ].

An interesting special case of almost pure inductive reasoning is epidemiology, where hypothesis-driven reasoning is rare and instead, the fundamental question is whether data-based evidence is sufficient to associate health risks with specific causes [ 31 , 34 ].

Recent advances in machine learning and “big-data” mining have driven the use of inductive reasoning to unprecedented heights. As an example, machine learning can greatly assist in the discovery of patterns, for instance, in biological sequences [ 40 ]. Going a step further, a pithy article by Andersen [ 41 ] proffered that we may not need to look for causality or mechanistic explanations anymore if we just have enough correlation: “With enough data, the numbers speak for themselves, correlation replaces causation, and science can advance even without coherent models or unified theories.”

Of course, the proposal to abandon the quest for causality caused pushback on philosophical as well as mathematical grounds. Allen [ 10 , 35 ] considered the idea “absurd” that data analysis could enhance understanding in the absence of a hypothesis. He felt confident “that even the formidable combination of computing power with ease of access to data cannot produce a qualitative shift in the way that we do science: the making of hypotheses remains an indispensable component in the growth of knowledge” [ 36 ]. Succi and Coveney [ 42 ] refuted the “most extravagant claims” of big-data proponents very differently, namely by analyzing the theories on which machine learning is founded. They contrasted the assumptions underlying these theories, such as the law of large numbers, with the mathematical reality of complex biological systems. Specifically, they carefully identified genuine features of these systems, such as nonlinearities, nonlocality of effects, fractal aspects, and high dimensionality, and argued that they fundamentally violate some of the statistical assumptions implicitly underlying big-data analysis, like independence of events. They concluded that these discrepancies “may lead to false expectations and, at their nadir, even to dangerous social, economical and political manipulation.” To ameliorate the situation, the field of big-data analysis would need new strong theorems characterizing the validity of its methods and the numbers of data required for obtaining reliable insights. Succi and Coveney go as far as stating that too many data are just as bad as insufficient data [ 42 ].

While philosophical doubts regarding inductive methods will always persist, one cannot deny that -omics-based, high-throughput studies, combined with machine learning and big-data analysis, have been very successful [ 43 ]. Yes, induction cannot truly reveal general laws, no matter how large the datasets, but they do provide insights that are very different from what science had offered before and may at least suggest novel patterns, trends, or principles. As a case in point, if many transcriptomic studies indicate that a particular gene set is involved in certain classes of phenomena, there is probably some truth to the observation, even though it is not mathematically provable. Kepler’s laws of astronomy were arguably derived solely from inductive reasoning [ 34 ].

Notwithstanding the opposing views on inductive methods, successful strategies shape how we think about science. Thus, to take advantage of all experimental options while ensuring quality of research, we must not allow that “anything goes” but instead identify and characterize standard operating procedures and controls that render this emerging scientific method valid and reproducible. A laudable step in this direction was the wide acceptance of “minimum information about a microarray experiment” (MIAME) standards for microarray experiments [ 44 ].

A third dimension of the scientific method: Allochthonous reasoning

Parallel to the blossoming of molecular biology and the rapid rise in the power and availability of computing in the late 20th century, the use of mathematical and computational models became increasingly recognized as relevant and beneficial for understanding biological phenomena. Indeed, mathematical models eventually achieved cornerstone status in the new field of computational systems biology.

Mathematical modeling has been used as a tool of biological analysis for a long time [ 27 , 45 – 48 ]. Interesting for the discussion here is that the use of mathematical and computational modeling in biology follows a scientific approach that is distinctly different from the traditional and the data-driven methods, because it is distributed over two entirely separate domains of knowledge. One consists of the biological reality of DNA, elephants, and roses, whereas the other is the world of mathematics, which is governed by numbers, symbols, theorems, and abstract work protocols. Because the ways of thinking—and even the languages—are different in these two realms, I suggest calling this type of knowledge acquisition “allochthonous” (literally Greek: in or from a “piece of land different from where one is at home”; one could perhaps translate it into modern lingo as “outside one’s comfort zone”). De facto, most allochthonous reasoning in biology presently refers to mathematics and computing, but one might also consider, for instance, the application of methods from linguistics in the analysis of DNA sequences or proteins [ 49 ].

One could argue that biologists have employed “models” for a long time, for instance, in the form of “model organisms,” cell lines, or in vitro experiments, which more or less faithfully reflect features of the organisms of true interest but are easier to manipulate. However, this type of biological model use is rather different from allochthonous reasoning, as it does not leave the realm of biology and uses the same language and often similar methodologies.

A brief discussion of three experiences from our lab may illustrate the benefits of allochthonous reasoning. (1) In a case study of renal cell carcinoma, a dynamic model was able to explain an observed yet nonintuitive metabolic profile in terms of the enzymatic reaction steps that had been altered during the disease [ 50 ]. (2) A transcriptome analysis had identified several genes as displaying significantly different expression patterns during malaria infection in comparison to the state of health. Considered by themselves and focusing solely on genes coding for specific enzymes of purine metabolism, the findings showed patterns that did not make sense. However, integrating the changes in a dynamic model revealed that purine metabolism globally shifted, in response to malaria, from guanine compounds to adenine, inosine, and hypoxanthine [ 51 ]. (3) Data capturing the dynamics of malaria parasites suggested growth rates that were biologically impossible. Speculation regarding possible explanations led to the hypothesis that many parasite-harboring red blood cells might “hide” from circulation and therewith from detection in the blood stream. While experimental testing of the feasibility of the hypothesis would have been expensive, a dynamic model confirmed that such a concealment mechanism could indeed quantitatively explain the apparently very high growth rates [ 52 ]. In all three cases, the insights gained inductively from computational modeling would have been difficult to obtain purely with experimental laboratory methods. Purely deductive allochthonous reasoning is the ultimate goal of the search for design and operating principles [ 53 – 55 ], which strives to explain why certain structures or functions are employed by nature time and again. An example is a linear metabolic pathway, in which feedback inhibition is essentially always exerted on the first step [ 56 , 57 ]. This generality allows the deduction that a so far unstudied linear pathway is most likely (or even certain to be) inhibited at the first step. Not strictly deductive—but rather abductive—was a study in our lab in which we analyzed time series data with a mathematical model that allowed us to infer the most likely regulatory structure of a metabolic pathway [ 58 , 59 ].

A typical allochthonous investigation begins in the realm of biology with the formulation of a hypothesis ( Fig 3 ). Instead of testing this hypothesis with laboratory experiments, the system encompassing the hypothesis is moved into the realm of mathematics. This move requires two sets of ingredients. One set consists of the simplification and abstraction of the biological system: Any distracting details that seem unrelated to the hypothesis and its context are omitted or represented collectively with other details. This simplification step carries the greatest risk of the entire modeling approach, as omission of seemingly negligible but, in truth, important details can easily lead to wrong results. The second set of ingredients consists of correspondence rules that translate every biological component or process into the language of mathematics [ 60 , 61 ].

This mathematical and computational approach is distributed over two realms, which are connected by correspondence rules.

https://doi.org/10.1371/journal.pcbi.1007279.g003

Once the system is translated, it has become an entirely mathematical construct that can be analyzed purely with mathematical and computational means. The results of this analysis are also strictly mathematical. They typically consist of values of variables, magnitudes of processes, sensitivity patterns, signs of eigenvalues, or qualitative features like the onset of oscillations or the potential for limit cycles. Correspondence rules are used again to move these results back into the realm of biology. As an example, the mathematical result that “two eigenvalues have positive real parts” does not make much sense to many biologists, whereas the interpretation that “the system is not stable at the steady state in question” is readily explained. New biological insights may lead to new hypotheses, which are tested either by experiments or by returning once more to the realm of mathematics. The model design, diagnosis, refinements, and validation consist of several phases, which have been discussed widely in the biomathematical literature. Importantly, each iteration of a typical modeling analysis consists of a move from the biological to the mathematical realm and back.

The reasoning within the realm of mathematics is often deductive, in the form of an Aristotelian syllogism, such as the well-known “All men are mortal; Socrates is a man; therefore, Socrates is mortal.” However, the reasoning may also be inductive, as it is the case with large-scale Monte-Carlo simulations that generate arbitrarily many “observations,” although they cannot reveal universal principles or theorems. An example is a simulation randomly drawing numbers in an attempt to show that every real number has an inverse. The simulation will always attest to this hypothesis but fail to discover the truth because it will never randomly draw 0. Generically, computational models may be considered sets of hypotheses, formulated as equations or as algorithms that reflect our perception of a complex system [ 27 ].

Impact of the multidimensional scientific method on learning

Almost all we know in biology has come from observation, experimentation, and interpretation. The traditional scientific method not only offered clear guidance for this knowledge gathering, but it also fundamentally shaped the way we think about the exploration of nature. When presented with a new research question, scientists were trained to think immediately in terms of hypotheses and alternatives, pondering the best feasible ways of testing them, and designing in their minds strong controls that would limit the effects of known or unknown confounders. Shaped by the rigidity of this ever-repeating process, our thinking became trained to move forward one well-planned step at a time. This modus operandi was rigid and exact. It also minimized the erroneous pursuit of long speculative lines of thought, because every step required testing before a new hypothesis was formed. While effective, the process was also very slow and driven by ingenuity—as well as bias—on the scientist’s part. This bias was sometimes a hindrance to necessary paradigm shifts [ 22 ].

High-throughput data generation, big-data analysis, and mathematical-computational modeling changed all that within a few decades. In particular, the acceptance of inductive principles and of the allochthonous use of nonbiological strategies to answer biological questions created an unprecedented mix of successes and chaos. To the horror of traditionalists, the importance of hypotheses became minimized, and the suggestion spread that the data would speak for themselves [ 36 ]. Importantly, within this fog of “anything goes,” the fundamental question arose how to determine whether an experiment was valid.

Because agreed-upon operating procedures affect research progress and interpretation, thinking, teaching, and sharing of results, this question requires a deconvolution of scientific strategies. Here I proffer that the single scientific method of the past should be expanded toward a vector space of scientific methods, with spanning vectors that correspond to different dimensions of the scientific method ( Fig 4 ).

The traditional hypothesis-based deductive scientific method is expanded into a 3D space that allows for synergistic blends of methods that include data-mining–inspired, inductive knowledge acquisition, and mathematical model-based, allochthonous reasoning.

https://doi.org/10.1371/journal.pcbi.1007279.g004

Obviously, all three dimensions have their advantages and drawbacks. The traditional, hypothesis-driven deductive method is philosophically “clean,” except that it is confounded by preconceptions and assumptions. The data-mining–inspired inductive method cannot offer universal truths but helps us explore very large spaces of factors that contribute to a phenomenon. Allochthonous, model-based reasoning can be performed mentally, with paper and pencil, through rigorous analysis, or with a host of computational methods that are precise and disprovable [ 27 ]. At the same time, they are incomparable faster, cheaper, and much more comprehensive than experiments in molecular biology. This reduction in cost and time, and the increase in coverage, may eventually have far-reaching consequences, as we can already fathom from much of modern physics.

Due to its long history, the traditional dimension of the scientific method is supported by clear and very strong standard operating procedures. Similarly, strong procedures need to be developed for the other two dimensions. The MIAME rules for microarray analysis provide an excellent example [ 44 ]. On the mathematical modeling front, no such rules are generally accepted yet, but trends toward them seem to emerge at the horizon. For instance, it seems to be becoming common practice to include sensitivity analyses in typical modeling studies and to assess the identifiability or sloppiness of ensembles of parameter combinations that fit a given dataset well [ 62 , 63 ].

From a philosophical point of view, it seems unlikely that objections against inductive reasoning will disappear. However, instead of pitting hypothesis-based deductive reasoning against inductivism, it seems more beneficial to determine how the different methods can be synergistically blended ( cf . [ 18 , 27 , 34 , 42 ]) as linear combinations of the three vectors of knowledge acquisition ( Fig 4 ). It is at this point unclear to what degree the identified three dimensions are truly independent of each other, whether additional dimensions should be added [ 24 ], or whether the different versions could be amalgamated into a single scientific method [ 18 ], especially if it is loosely defined as a form of critical thinking [ 8 ]. Nobel Laureate Percy Bridgman even concluded that “science is what scientists do, and there are as many scientific methods as there are individual scientists” [ 8 , 64 ].

Combinations of the three spanning vectors of the scientific method have been emerging for some time. Many biologists already use inductive high-throughput methods to develop specific hypotheses that are subsequently tested with deductive or further inductive methods [ 34 , 65 ]. In terms of including mathematical modeling, physics and geology have been leading the way for a long time, often by beginning an investigation in theory, before any actual experiment is performed. It will benefit biology to look into this strategy and to develop best practices of allochthonous reasoning.

The blending of methods may take quite different shapes. Early on, Ideker and colleagues [ 65 ] proposed an integrated experimental approach for pathway analysis that offered a glimpse of new experimental strategies within the space of scientific methods. In a similar vein, Covert and colleagues [ 66 ] included computational methods into such an integrated approach. Additional examples of blended analyses in systems biology can be seen in other works, such as [ 43 , 67 – 73 ]. Generically, it is often beneficial to start with big data, determine patterns in associations and correlations, then switch to the mathematical realm in order to filter out spurious correlations in a high-throughput fashion. If this procedure is executed in an iterative manner, the “surviving” associations have an increased level of confidence and are good candidates for further experimental or computational testing (personal communication from S. Chandrasekaran).

If each component of a blended scientific method follows strict, commonly agreed guidelines, “linear combinations” within the 3D space can also be checked objectively, per deconvolution. In addition, guidelines for synergistic blends of component procedures should be developed. If we carefully monitor such blends, time will presumably indicate which method is best for which task and how the different approaches optimally inform each other. For instance, it will be interesting to study whether there is an optimal sequence of experiments along the three axes for a particular class of tasks. Big-data analysis together with inductive reasoning might be optimal for creating initial hypotheses and possibly refuting wrong speculations (“we had thought this gene would be involved, but apparently it isn’t”). If the logic of an emerging hypotheses can be tested with mathematical and computational tools, it will almost certainly be faster and cheaper than an immediate launch into wet-lab experimentation. It is also likely that mathematical reasoning will be able to refute some apparently feasible hypothesis and suggest amendments. Ultimately, the “surviving” hypotheses must still be tested for validity through conventional experiments. Deconvolving current practices and optimizing the combination of methods within the 3D or higher-dimensional space of scientific methods will likely result in better planning of experiments and in synergistic blends of approaches that have the potential capacity of addressing some of the grand challenges in biology.

Acknowledgments

The author is very grateful to Dr. Sriram Chandrasekaran and Ms. Carla Kumbale for superb suggestions and invaluable feedback.

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What is a scientific hypothesis?

It's the initial building block in the scientific method.

Hypothesis basics

What makes a hypothesis testable.

• Types of hypotheses
• Hypothesis versus theory

Additional resources

Bibliography.

A scientific hypothesis is a tentative, testable explanation for a phenomenon in the natural world. It's the initial building block in the scientific method . Many describe it as an "educated guess" based on prior knowledge and observation. While this is true, a hypothesis is more informed than a guess. While an "educated guess" suggests a random prediction based on a person's expertise, developing a hypothesis requires active observation and background research. 

The basic idea of a hypothesis is that there is no predetermined outcome. For a solution to be termed a scientific hypothesis, it has to be an idea that can be supported or refuted through carefully crafted experimentation or observation. This concept, called falsifiability and testability, was advanced in the mid-20th century by Austrian-British philosopher Karl Popper in his famous book "The Logic of Scientific Discovery" (Routledge, 1959).

A key function of a hypothesis is to derive predictions about the results of future experiments and then perform those experiments to see whether they support the predictions.

A hypothesis is usually written in the form of an if-then statement, which gives a possibility (if) and explains what may happen because of the possibility (then). The statement could also include "may," according to California State University, Bakersfield .

Here are some examples of hypothesis statements:

• If garlic repels fleas, then a dog that is given garlic every day will not get fleas.
• If sugar causes cavities, then people who eat a lot of candy may be more prone to cavities.
• If ultraviolet light can damage the eyes, then maybe this light can cause blindness.

A useful hypothesis should be testable and falsifiable. That means that it should be possible to prove it wrong. A theory that can't be proved wrong is nonscientific, according to Karl Popper's 1963 book " Conjectures and Refutations ."

An example of an untestable statement is, "Dogs are better than cats." That's because the definition of "better" is vague and subjective. However, an untestable statement can be reworded to make it testable. For example, the previous statement could be changed to this: "Owning a dog is associated with higher levels of physical fitness than owning a cat." With this statement, the researcher can take measures of physical fitness from dog and cat owners and compare the two.

Types of scientific hypotheses

In an experiment, researchers generally state their hypotheses in two ways. The null hypothesis predicts that there will be no relationship between the variables tested, or no difference between the experimental groups. The alternative hypothesis predicts the opposite: that there will be a difference between the experimental groups. This is usually the hypothesis scientists are most interested in, according to the University of Miami .

For example, a null hypothesis might state, "There will be no difference in the rate of muscle growth between people who take a protein supplement and people who don't." The alternative hypothesis would state, "There will be a difference in the rate of muscle growth between people who take a protein supplement and people who don't."

If the results of the experiment show a relationship between the variables, then the null hypothesis has been rejected in favor of the alternative hypothesis, according to the book " Research Methods in Psychology " (​​BCcampus, 2015). 

There are other ways to describe an alternative hypothesis. The alternative hypothesis above does not specify a direction of the effect, only that there will be a difference between the two groups. That type of prediction is called a two-tailed hypothesis. If a hypothesis specifies a certain direction — for example, that people who take a protein supplement will gain more muscle than people who don't — it is called a one-tailed hypothesis, according to William M. K. Trochim , a professor of Policy Analysis and Management at Cornell University.

Sometimes, errors take place during an experiment. These errors can happen in one of two ways. A type I error is when the null hypothesis is rejected when it is true. This is also known as a false positive. A type II error occurs when the null hypothesis is not rejected when it is false. This is also known as a false negative, according to the University of California, Berkeley . 

A hypothesis can be rejected or modified, but it can never be proved correct 100% of the time. For example, a scientist can form a hypothesis stating that if a certain type of tomato has a gene for red pigment, that type of tomato will be red. During research, the scientist then finds that each tomato of this type is red. Though the findings confirm the hypothesis, there may be a tomato of that type somewhere in the world that isn't red. Thus, the hypothesis is true, but it may not be true 100% of the time.

Scientific theory vs. scientific hypothesis

The best hypotheses are simple. They deal with a relatively narrow set of phenomena. But theories are broader; they generally combine multiple hypotheses into a general explanation for a wide range of phenomena, according to the University of California, Berkeley . For example, a hypothesis might state, "If animals adapt to suit their environments, then birds that live on islands with lots of seeds to eat will have differently shaped beaks than birds that live on islands with lots of insects to eat." After testing many hypotheses like these, Charles Darwin formulated an overarching theory: the theory of evolution by natural selection.

"Theories are the ways that we make sense of what we observe in the natural world," Tanner said. "Theories are structures of ideas that explain and interpret facts." 

• Read more about writing a hypothesis, from the American Medical Writers Association.
• Find out why a hypothesis isn't always necessary in science, from The American Biology Teacher.
• Learn about null and alternative hypotheses, from Prof. Essa on YouTube .

Encyclopedia Britannica. Scientific Hypothesis. Jan. 13, 2022. https://www.britannica.com/science/scientific-hypothesis

Karl Popper, "The Logic of Scientific Discovery," Routledge, 1959.

California State University, Bakersfield, "Formatting a testable hypothesis." https://www.csub.edu/~ddodenhoff/Bio100/Bio100sp04/formattingahypothesis.htm  

Karl Popper, "Conjectures and Refutations," Routledge, 1963.

Price, P., Jhangiani, R., & Chiang, I., "Research Methods of Psychology — 2nd Canadian Edition," BCcampus, 2015.‌

University of Miami, "The Scientific Method" http://www.bio.miami.edu/dana/161/evolution/161app1_scimethod.pdf  

William M.K. Trochim, "Research Methods Knowledge Base," https://conjointly.com/kb/hypotheses-explained/  

University of California, Berkeley, "Multiple Hypothesis Testing and False Discovery Rate" https://www.stat.berkeley.edu/~hhuang/STAT141/Lecture-FDR.pdf  

University of California, Berkeley, "Science at multiple levels" https://undsci.berkeley.edu/article/0_0_0/howscienceworks_19

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What is the Scientific Method: How does it work and why is it important?

The scientific method is a systematic process involving steps like defining questions, forming hypotheses, conducting experiments, and analyzing data. It minimizes biases and enables replicable research, leading to groundbreaking discoveries like Einstein's theory of relativity, penicillin, and the structure of DNA. This ongoing approach promotes reason, evidence, and the pursuit of truth in science.

Updated on November 18, 2023

Beginning in elementary school, we are exposed to the scientific method and taught how to put it into practice. As a tool for learning, it prepares children to think logically and use reasoning when seeking answers to questions.

Rather than jumping to conclusions, the scientific method gives us a recipe for exploring the world through observation and trial and error. We use it regularly, sometimes knowingly in academics or research, and sometimes subconsciously in our daily lives.

In this article we will refresh our memories on the particulars of the scientific method, discussing where it comes from, which elements comprise it, and how it is put into practice. Then, we will consider the importance of the scientific method, who uses it and under what circumstances.

What is the scientific method?

The scientific method is a dynamic process that involves objectively investigating questions through observation and experimentation . Applicable to all scientific disciplines, this systematic approach to answering questions is more accurately described as a flexible set of principles than as a fixed series of steps.

The following representations of the scientific method illustrate how it can be both condensed into broad categories and also expanded to reveal more and more details of the process. These graphics capture the adaptability that makes this concept universally valuable as it is relevant and accessible not only across age groups and educational levels but also within various contexts.

Steps in the scientific method

While the scientific method is versatile in form and function, it encompasses a collection of principles that create a logical progression to the process of problem solving:

• Define a question : Constructing a clear and precise problem statement that identifies the main question or goal of the investigation is the first step. The wording must lend itself to experimentation by posing a question that is both testable and measurable.
• Gather information and resources : Researching the topic in question to find out what is already known and what types of related questions others are asking is the next step in this process. This background information is vital to gaining a full understanding of the subject and in determining the best design for experiments. 
• Form a hypothesis : Composing a concise statement that identifies specific variables and potential results, which can then be tested, is a crucial step that must be completed before any experimentation. An imperfection in the composition of a hypothesis can result in weaknesses to the entire design of an experiment.
• Perform the experiments : Testing the hypothesis by performing replicable experiments and collecting resultant data is another fundamental step of the scientific method. By controlling some elements of an experiment while purposely manipulating others, cause and effect relationships are established.
• Analyze the data : Interpreting the experimental process and results by recognizing trends in the data is a necessary step for comprehending its meaning and supporting the conclusions. Drawing inferences through this systematic process lends substantive evidence for either supporting or rejecting the hypothesis.
• Report the results : Sharing the outcomes of an experiment, through an essay, presentation, graphic, or journal article, is often regarded as a final step in this process. Detailing the project's design, methods, and results not only promotes transparency and replicability but also adds to the body of knowledge for future research.
• Retest the hypothesis : Repeating experiments to see if a hypothesis holds up in all cases is a step that is manifested through varying scenarios. Sometimes a researcher immediately checks their own work or replicates it at a future time, or another researcher will repeat the experiments to further test the hypothesis.

Where did the scientific method come from?

Oftentimes, ancient peoples attempted to answer questions about the unknown by:

• Making simple observations
• Discussing the possibilities with others deemed worthy of a debate
• Drawing conclusions based on dominant opinions and preexisting beliefs

For example, take Greek and Roman mythology. Myths were used to explain everything from the seasons and stars to the sun and death itself.

However, as societies began to grow through advancements in agriculture and language, ancient civilizations like Egypt and Babylonia shifted to a more rational analysis for understanding the natural world. They increasingly employed empirical methods of observation and experimentation that would one day evolve into the scientific method . 

In the 4th century, Aristotle, considered the Father of Science by many, suggested these elements , which closely resemble the contemporary scientific method, as part of his approach for conducting science:

• Study what others have written about the subject.
• Look for the general consensus about the subject.
• Perform a systematic study of everything even partially related to the topic.

By continuing to emphasize systematic observation and controlled experiments, scholars such as Al-Kindi and Ibn al-Haytham helped expand this concept throughout the Islamic Golden Age . 

In his 1620 treatise, Novum Organum , Sir Francis Bacon codified the scientific method, arguing not only that hypotheses must be tested through experiments but also that the results must be replicated to establish a truth. Coming at the height of the Scientific Revolution, this text made the scientific method accessible to European thinkers like Galileo and Isaac Newton who then put the method into practice.

As science modernized in the 19th century, the scientific method became more formalized, leading to significant breakthroughs in fields such as evolution and germ theory. Today, it continues to evolve, underpinning scientific progress in diverse areas like quantum mechanics, genetics, and artificial intelligence.

Why is the scientific method important?

The history of the scientific method illustrates how the concept developed out of a need to find objective answers to scientific questions by overcoming biases based on fear, religion, power, and cultural norms. This still holds true today.

By implementing this standardized approach to conducting experiments, the impacts of researchers’ personal opinions and preconceived notions are minimized. The organized manner of the scientific method prevents these and other mistakes while promoting the replicability and transparency necessary for solid scientific research.

The importance of the scientific method is best observed through its successes, for example: 

• “ Albert Einstein stands out among modern physicists as the scientist who not only formulated a theory of revolutionary significance but also had the genius to reflect in a conscious and technical way on the scientific method he was using.” Devising a hypothesis based on the prevailing understanding of Newtonian physics eventually led Einstein to devise the theory of general relativity .
• Howard Florey “Perhaps the most useful lesson which has come out of the work on penicillin has been the demonstration that success in this field depends on the development and coordinated use of technical methods.” After discovering a mold that prevented the growth of Staphylococcus bacteria, Dr. Alexander Flemimg designed experiments to identify and reproduce it in the lab, thus leading to the development of penicillin .
• James D. Watson “Every time you understand something, religion becomes less likely. Only with the discovery of the double helix and the ensuing genetic revolution have we had grounds for thinking that the powers held traditionally to be the exclusive property of the gods might one day be ours. . . .” By using wire models to conceive a structure for DNA, Watson and Crick crafted a hypothesis for testing combinations of amino acids, X-ray diffraction images, and the current research in atomic physics, resulting in the discovery of DNA’s double helix structure .

Final thoughts

As the cases exemplify, the scientific method is never truly completed, but rather started and restarted. It gave these researchers a structured process that was easily replicated, modified, and built upon. 

While the scientific method may “end” in one context, it never literally ends. When a hypothesis, design, methods, and experiments are revisited, the scientific method simply picks up where it left off. Each time a researcher builds upon previous knowledge, the scientific method is restored with the pieces of past efforts.

By guiding researchers towards objective results based on transparency and reproducibility, the scientific method acts as a defense against bias, superstition, and preconceived notions. As we embrace the scientific method's enduring principles, we ensure that our quest for knowledge remains firmly rooted in reason, evidence, and the pursuit of truth.

The AJE Team

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What Is a Hypothesis? (Science)

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• Ph.D., Biomedical Sciences, University of Tennessee at Knoxville
• B.A., Physics and Mathematics, Hastings College

A hypothesis (plural hypotheses) is a proposed explanation for an observation. The definition depends on the subject.

In science, a hypothesis is part of the scientific method. It is a prediction or explanation that is tested by an experiment. Observations and experiments may disprove a scientific hypothesis, but can never entirely prove one.

In the study of logic, a hypothesis is an if-then proposition, typically written in the form, "If X , then Y ."

In common usage, a hypothesis is simply a proposed explanation or prediction, which may or may not be tested.

Writing a Hypothesis

Most scientific hypotheses are proposed in the if-then format because it's easy to design an experiment to see whether or not a cause and effect relationship exists between the independent variable and the dependent variable . The hypothesis is written as a prediction of the outcome of the experiment.

• Null Hypothesis and Alternative Hypothesis

Statistically, it's easier to show there is no relationship between two variables than to support their connection. So, scientists often propose the null hypothesis . The null hypothesis assumes changing the independent variable will have no effect on the dependent variable.

In contrast, the alternative hypothesis suggests changing the independent variable will have an effect on the dependent variable. Designing an experiment to test this hypothesis can be trickier because there are many ways to state an alternative hypothesis.

For example, consider a possible relationship between getting a good night's sleep and getting good grades. The null hypothesis might be stated: "The number of hours of sleep students get is unrelated to their grades" or "There is no correlation between hours of sleep and grades."

An experiment to test this hypothesis might involve collecting data, recording average hours of sleep for each student and grades. If a student who gets eight hours of sleep generally does better than students who get four hours of sleep or 10 hours of sleep, the hypothesis might be rejected.

But the alternative hypothesis is harder to propose and test. The most general statement would be: "The amount of sleep students get affects their grades." The hypothesis might also be stated as "If you get more sleep, your grades will improve" or "Students who get nine hours of sleep have better grades than those who get more or less sleep."

In an experiment, you can collect the same data, but the statistical analysis is less likely to give you a high confidence limit.

Usually, a scientist starts out with the null hypothesis. From there, it may be possible to propose and test an alternative hypothesis, to narrow down the relationship between the variables.

Example of a Hypothesis

Examples of a hypothesis include:

• If you drop a rock and a feather, (then) they will fall at the same rate.
• Plants need sunlight in order to live. (if sunlight, then life)
• Eating sugar gives you energy. (if sugar, then energy)
• White, Jay D.  Research in Public Administration . Conn., 1998.
• Schick, Theodore, and Lewis Vaughn.  How to Think about Weird Things: Critical Thinking for a New Age . McGraw-Hill Higher Education, 2002.
• Null Hypothesis Definition and Examples
• Definition of a Hypothesis
• What Are the Elements of a Good Hypothesis?
• Six Steps of the Scientific Method
• Independent Variable Definition and Examples
• What Are Examples of a Hypothesis?
• Understanding Simple vs Controlled Experiments
• Scientific Method Flow Chart
• Scientific Method Vocabulary Terms
• What Is a Testable Hypothesis?
• Null Hypothesis Examples
• What 'Fail to Reject' Means in a Hypothesis Test
• How To Design a Science Fair Experiment
• What Is an Experiment? Definition and Design
• Hypothesis Test for the Difference of Two Population Proportions

1.2 The Process of Science

Learning objectives.

• Identify the shared characteristics of the natural sciences
• Understand the process of scientific inquiry
• Compare inductive reasoning with deductive reasoning
• Describe the goals of basic science and applied science

Like geology, physics, and chemistry, biology is a science that gathers knowledge about the natural world. Specifically, biology is the study of life. The discoveries of biology are made by a community of researchers who work individually and together using agreed-on methods. In this sense, biology, like all sciences is a social enterprise like politics or the arts. The methods of science include careful observation, record keeping, logical and mathematical reasoning, experimentation, and submitting conclusions to the scrutiny of others. Science also requires considerable imagination and creativity; a well-designed experiment is commonly described as elegant, or beautiful. Like politics, science has considerable practical implications and some science is dedicated to practical applications, such as the prevention of disease (see Figure 1.15 ). Other science proceeds largely motivated by curiosity. Whatever its goal, there is no doubt that science, including biology, has transformed human existence and will continue to do so.

The Nature of Science

Biology is a science, but what exactly is science? What does the study of biology share with other scientific disciplines? Science (from the Latin scientia, meaning "knowledge") can be defined as knowledge about the natural world.

Science is a very specific way of learning, or knowing, about the world. The history of the past 500 years demonstrates that science is a very powerful way of knowing about the world; it is largely responsible for the technological revolutions that have taken place during this time. There are however, areas of knowledge and human experience that the methods of science cannot be applied to. These include such things as answering purely moral questions, aesthetic questions, or what can be generally categorized as spiritual questions. Science cannot investigate these areas because they are outside the realm of material phenomena, the phenomena of matter and energy, and cannot be observed and measured.

The scientific method is a method of research with defined steps that include experiments and careful observation. The steps of the scientific method will be examined in detail later, but one of the most important aspects of this method is the testing of hypotheses. A hypothesis is a suggested explanation for an event, which can be tested. Hypotheses, or tentative explanations, are generally produced within the context of a scientific theory . A generally accepted scientific theory is thoroughly tested and confirmed explanation for a set of observations or phenomena. Scientific theory is the foundation of scientific knowledge. In addition, in many scientific disciplines (less so in biology) there are scientific laws , often expressed in mathematical formulas, which describe how elements of nature will behave under certain specific conditions. There is not an evolution of hypotheses through theories to laws as if they represented some increase in certainty about the world. Hypotheses are the day-to-day material that scientists work with and they are developed within the context of theories. Laws are concise descriptions of parts of the world that are amenable to formulaic or mathematical description.

Natural Sciences

What would you expect to see in a museum of natural sciences? Frogs? Plants? Dinosaur skeletons? Exhibits about how the brain functions? A planetarium? Gems and minerals? Or maybe all of the above? Science includes such diverse fields as astronomy, biology, computer sciences, geology, logic, physics, chemistry, and mathematics ( Figure 1.16 ). However, those fields of science related to the physical world and its phenomena and processes are considered natural sciences . Thus, a museum of natural sciences might contain any of the items listed above.

There is no complete agreement when it comes to defining what the natural sciences include. For some experts, the natural sciences are astronomy, biology, chemistry, earth science, and physics. Other scholars choose to divide natural sciences into life sciences , which study living things and include biology, and physical sciences , which study nonliving matter and include astronomy, physics, and chemistry. Some disciplines such as biophysics and biochemistry build on two sciences and are interdisciplinary.

Scientific Inquiry

One thing is common to all forms of science: an ultimate goal “to know.” Curiosity and inquiry are the driving forces for the development of science. Scientists seek to understand the world and the way it operates. Two methods of logical thinking are used: inductive reasoning and deductive reasoning.

Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. This type of reasoning is common in descriptive science. A life scientist such as a biologist makes observations and records them. These data can be qualitative (descriptive) or quantitative (consisting of numbers), and the raw data can be supplemented with drawings, pictures, photos, or videos. From many observations, the scientist can infer conclusions (inductions) based on evidence. Inductive reasoning involves formulating generalizations inferred from careful observation and the analysis of a large amount of data. Brain studies often work this way. Many brains are observed while people are doing a task. The part of the brain that lights up, indicating activity, is then demonstrated to be the part controlling the response to that task.

Deductive reasoning or deduction is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning. Deductive reasoning is a form of logical thinking that uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid. For example, a prediction would be that if the climate is becoming warmer in a region, the distribution of plants and animals should change. Comparisons have been made between distributions in the past and the present, and the many changes that have been found are consistent with a warming climate. Finding the change in distribution is evidence that the climate change conclusion is a valid one.

Both types of logical thinking are related to the two main pathways of scientific study: descriptive science and hypothesis-based science. Descriptive (or discovery) science aims to observe, explore, and discover, while hypothesis-based science begins with a specific question or problem and a potential answer or solution that can be tested. The boundary between these two forms of study is often blurred, because most scientific endeavors combine both approaches. Observations lead to questions, questions lead to forming a hypothesis as a possible answer to those questions, and then the hypothesis is tested. Thus, descriptive science and hypothesis-based science are in continuous dialogue.

Hypothesis Testing

Biologists study the living world by posing questions about it and seeking science-based responses. This approach is common to other sciences as well and is often referred to as the scientific method. The scientific method was used even in ancient times, but it was first documented by England’s Sir Francis Bacon (1561–1626) ( Figure 1.17 ), who set up inductive methods for scientific inquiry. The scientific method is not exclusively used by biologists but can be applied to almost anything as a logical problem-solving method.

The scientific process typically starts with an observation (often a problem to be solved) that leads to a question. Let’s think about a simple problem that starts with an observation and apply the scientific method to solve the problem. One Monday morning, a student arrives at class and quickly discovers that the classroom is too warm. That is an observation that also describes a problem: the classroom is too warm. The student then asks a question: “Why is the classroom so warm?”

Recall that a hypothesis is a suggested explanation that can be tested. To solve a problem, several hypotheses may be proposed. For example, one hypothesis might be, “The classroom is warm because no one turned on the air conditioning.” But there could be other responses to the question, and therefore other hypotheses may be proposed. A second hypothesis might be, “The classroom is warm because there is a power failure, and so the air conditioning doesn’t work.”

Once a hypothesis has been selected, a prediction may be made. A prediction is similar to a hypothesis but it typically has the format “If . . . then . . . .” For example, the prediction for the first hypothesis might be, “ If the student turns on the air conditioning, then the classroom will no longer be too warm.”

A hypothesis must be testable to ensure that it is valid. For example, a hypothesis that depends on what a bear thinks is not testable, because it can never be known what a bear thinks. It should also be falsifiable , meaning that it can be disproven by experimental results. An example of an unfalsifiable hypothesis is “Botticelli’s Birth of Venus is beautiful.” There is no experiment that might show this statement to be false. To test a hypothesis, a researcher will conduct one or more experiments designed to eliminate one or more of the hypotheses. This is important. A hypothesis can be disproven, or eliminated, but it can never be proven. Science does not deal in proofs like mathematics. If an experiment fails to disprove a hypothesis, then we find support for that explanation, but this is not to say that down the road a better explanation will not be found, or a more carefully designed experiment will be found to falsify the hypothesis.

Each experiment will have one or more variables and one or more controls. A variable is any part of the experiment that can vary or change during the experiment. A control is a part of the experiment that does not change. Look for the variables and controls in the example that follows. As a simple example, an experiment might be conducted to test the hypothesis that phosphate limits the growth of algae in freshwater ponds. A series of artificial ponds are filled with water and half of them are treated by adding phosphate each week, while the other half are treated by adding a salt that is known not to be used by algae. The variable here is the phosphate (or lack of phosphate), the experimental or treatment cases are the ponds with added phosphate and the control ponds are those with something inert added, such as the salt. Just adding something is also a control against the possibility that adding extra matter to the pond has an effect. If the treated ponds show lesser growth of algae, then we have found support for our hypothesis. If they do not, then we reject our hypothesis. Be aware that rejecting one hypothesis does not determine whether or not the other hypotheses can be accepted; it simply eliminates one hypothesis that is not valid ( Figure 1.18 ). Using the scientific method, the hypotheses that are inconsistent with experimental data are rejected.

In recent years a new approach of testing hypotheses has developed as a result of an exponential growth of data deposited in various databases. Using computer algorithms and statistical analyses of data in databases, a new field of so-called "data research" (also referred to as "in silico" research) provides new methods of data analyses and their interpretation. This will increase the demand for specialists in both biology and computer science, a promising career opportunity.

Visual Connection

In the example below, the scientific method is used to solve an everyday problem. Which part in the example below is the hypothesis? Which is the prediction? Based on the results of the experiment, is the hypothesis supported? If it is not supported, propose some alternative hypotheses.

• My toaster doesn’t toast my bread.
• Why doesn’t my toaster work?
• There is something wrong with the electrical outlet.
• If something is wrong with the outlet, my coffeemaker also won’t work when plugged into it.
• I plug my coffeemaker into the outlet.
• My coffeemaker works.

In practice, the scientific method is not as rigid and structured as it might at first appear. Sometimes an experiment leads to conclusions that favor a change in approach; often, an experiment brings entirely new scientific questions to the puzzle. Many times, science does not operate in a linear fashion; instead, scientists continually draw inferences and make generalizations, finding patterns as their research proceeds. Scientific reasoning is more complex than the scientific method alone suggests.

Basic and Applied Science

The scientific community has been debating for the last few decades about the value of different types of science. Is it valuable to pursue science for the sake of simply gaining knowledge, or does scientific knowledge only have worth if we can apply it to solving a specific problem or bettering our lives? This question focuses on the differences between two types of science: basic science and applied science.

Basic science or “pure” science seeks to expand knowledge regardless of the short-term application of that knowledge. It is not focused on developing a product or a service of immediate public or commercial value. The immediate goal of basic science is knowledge for knowledge’s sake, though this does not mean that in the end it may not result in an application.

In contrast, applied science or “technology,” aims to use science to solve real-world problems, making it possible, for example, to improve a crop yield, find a cure for a particular disease, or save animals threatened by a natural disaster. In applied science, the problem is usually defined for the researcher.

Some individuals may perceive applied science as “useful” and basic science as “useless.” A question these people might pose to a scientist advocating knowledge acquisition would be, “What for?” A careful look at the history of science, however, reveals that basic knowledge has resulted in many remarkable applications of great value. Many scientists think that a basic understanding of science is necessary before an application is developed; therefore, applied science relies on the results generated through basic science. Other scientists think that it is time to move on from basic science and instead to find solutions to actual problems. Both approaches are valid. It is true that there are problems that demand immediate attention; however, few solutions would be found without the help of the knowledge generated through basic science.

One example of how basic and applied science can work together to solve practical problems occurred after the discovery of DNA structure led to an understanding of the molecular mechanisms governing DNA replication. Strands of DNA, unique in every human, are found in our cells, where they provide the instructions necessary for life. During DNA replication, new copies of DNA are made, shortly before a cell divides to form new cells. Understanding the mechanisms of DNA replication enabled scientists to develop laboratory techniques that are now used to identify genetic diseases, pinpoint individuals who were at a crime scene, and determine paternity. Without basic science, it is unlikely that applied science could exist.

Another example of the link between basic and applied research is the Human Genome Project, a study in which each human chromosome was analyzed and mapped to determine the precise sequence of DNA subunits and the exact location of each gene. (The gene is the basic unit of heredity represented by a specific DNA segment that codes for a functional molecule.) Other organisms have also been studied as part of this project to gain a better understanding of human chromosomes. The Human Genome Project ( Figure 1.19 ) relied on basic research carried out with non-human organisms and, later, with the human genome. An important end goal eventually became using the data for applied research seeking cures for genetically related diseases.

While research efforts in both basic science and applied science are usually carefully planned, it is important to note that some discoveries are made by serendipity, that is, by means of a fortunate accident or a lucky surprise. Penicillin was discovered when biologist Alexander Fleming accidentally left a petri dish of Staphylococcus bacteria open. An unwanted mold grew, killing the bacteria. The mold turned out to be Penicillium , and a new critically important antibiotic was discovered. In a similar manner, Percy Lavon Julian was an established medicinal chemist working on a way to mass produce compounds with which to manufacture important drugs. He was focused on using soybean oil in the production of progesterone (a hormone important in the menstrual cycle and pregnancy), but it wasn't until water accidentally leaked into a large soybean oil storage tank that he found his method. Immediately recognizing the resulting substance as stigmasterol, a primary ingredient in progesterone and similar drugs, he began the process of replicating and industrializing the process in a manner that has helped millions of people. Even in the highly organized world of science, luck—when combined with an observant, curious mind focused on the types of reasoning discussed above—can lead to unexpected breakthroughs.

Reporting Scientific Work

Whether scientific research is basic science or applied science, scientists must share their findings for other researchers to expand and build upon their discoveries. Communication and collaboration within and between sub disciplines of science are key to the advancement of knowledge in science. For this reason, an important aspect of a scientist’s work is disseminating results and communicating with peers. Scientists can share results by presenting them at a scientific meeting or conference, but this approach can reach only the limited few who are present. Instead, most scientists present their results in peer-reviewed articles that are published in scientific journals. Peer-reviewed articles are scientific papers that are reviewed, usually anonymously by a scientist’s colleagues, or peers. These colleagues are qualified individuals, often experts in the same research area, who judge whether or not the scientist’s work is suitable for publication. The process of peer review helps to ensure that the research described in a scientific paper or grant proposal is original, significant, logical, and thorough. Grant proposals, which are requests for research funding, are also subject to peer review. Scientists publish their work so other scientists can reproduce their experiments under similar or different conditions to expand on the findings.

There are many journals and the popular press that do not use a peer-review system. A large number of online open-access journals, journals with articles available without cost, are now available many of which use rigorous peer-review systems, but some of which do not. Results of any studies published in these forums without peer review are not reliable and should not form the basis for other scientific work. In one exception, journals may allow a researcher to cite a personal communication from another researcher about unpublished results with the cited author’s permission.

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• Authors: Samantha Fowler, Rebecca Roush, James Wise
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• Book title: Concepts of Biology
• Publication date: Apr 25, 2013
• Location: Houston, Texas
• Book URL: https://openstax.org/books/concepts-biology/pages/1-introduction
• Section URL: https://openstax.org/books/concepts-biology/pages/1-2-the-process-of-science

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• Published: 13 May 2024

Hypertonic saline- and detergent-accelerated EDTA-based decalcification better preserves mRNA of bones

• Zhongmin Li 1 ,
• Clara Wenhart 1 ,
• Andreas Reimann 1 ,
• Yi-Li Cho 1 ,
• Kristin Adler 1 &
• Goetz Muench 1  

Scientific Reports volume  14 , Article number:  10888 ( 2024 ) Cite this article

Metrics details

• Biological techniques
• Cell biology
• Medical research

Ethylenediaminetetraacetic acid (EDTA), a classically used chelating agent of decalcification, maintains good morphological details, but its slow decalcification limits its wider applications. Many procedures have been reported to accelerate EDTA-based decalcification, involving temperature, concentration, sonication, agitation, vacuum, microwave, or combination. However, these procedures, concentrating on purely tissue-outside physical factors to increase the chemical diffusion, do not enable EDTA to exert its full capacity due to tissue intrinsic chemical resistances around the diffusion passage. The resistances, such as tissue inner lipids and electric charges, impede the penetration of EDTA. We hypothesized that delipidation and shielding electric charges would accelerate EDTA-based penetration and the subsequent decalcification. The hypothesis was verified by the observation of speedy penetration of EDTA with additives of detergents and hypertonic saline, testing on tissue-mimicking gels of collagen and adult mouse bones. Using a 26% EDTA mixture with the additives at 45°C, a conventional 7-day decalcification of adult mouse ankle joints could be completed within 24 h while the tissue morphological structure, antigenicity, enzymes, and DNA were well preserved, and mRNA better retained compared to using 15% EDTA at room temperature. The addition of hypertonic saline and detergents to EDTA decalcification is a simple, rapid, and inexpensive method that doesn't disrupt the current histological workflow. This method is equally or even more effective than the currently most used decalcification methods in preserving the morphological details of tissues. It can be highly beneficial for the related community.

Introduction

Decalcification removes inorganic minerals from the organic matrix of bones, bone-containing specimens, and teeth and is routinely used in laboratories. The softened tissues after decalcification are compatible with routine paraffin-embedding and sectioning for accurate diagnoses in histopathology. The methods used for decalcification are often chosen to provide optimal outcomes for histological stains, immunostaining, in situ hybridization, or molecular tests. The highest quality is indicated by well-preserved tissue structures and cell details, adequate antigenicity, and high confidence in identifying molecular biomarkers or cell nuclear acids. However, the intact morphology of calcified tissue is often difficult to preserve following decalcification with acidic decalcifiers (e.g., formic acid, Hydrochloric acid, etc.) although they are widely used because they provide rapid decalcification. Exposure to the harsh chemicals of acidic decalcifiers is reported to damage the soft tissue structure and negatively affect cellular integrity, antigenicity, and the integrity of DNA 1 , 2 , 3 .

Ethylenediaminetetraacetic acid (EDTA), a classical chelating agent for decalcification, (1) reacts with calcium by binding with the ionized calcium on the outer layer of the apatite crystal, (2) has no effect on the surrounding tissue or tissue depleted of calcium 4 , 5 , and (3) accordingly provides suitable preservation of the tissue integrity and histological features, enzymes, antigenicity 6 , 7 , 8 , DNA and RNA 2 , 3 , 9 , 10 , 11 . The attributes of the EDTA-based decalcification, which are necessary for immunohistochemistry and in situ hybrid analysis, are more important nowadays, as molecular and immunological diagnostics have become part of the standard of care for patients with cancers, such as bone-metastasized or calcified cancers. However, its application has been mostly restricted owing to the slower decalcifying process than acids 5 , 12 . The time lag can be detrimental to both tissue morphology and antigenicity, can hinder productivity, and can delay diagnostic results in clinical settings 13 , 14 .

To shorten the time needed for EDTA-based decalcification, many additional treatments have been tested. These include raising the temperature 14 , 15 , agitation 15 , 16 , 17 , electric field 18 , pulsed electric field 19 , ultrasound 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , high pressures and vacuum 16 , sonication and irradiation of microwave 26 , 29 , 30 , 31 , 32 , 33 , high concentration 34 , etc. Theoretically, the outside physical factors mentioned above, which enhance the relevant agent diffusion inside tissue by purely mechanical means, would lead to accelerated decalcification. However, what is ignored in the reports, and maybe more important, is that the agents must reduce the intrinsic chemical resistance to diffusion into the tissues. Tissue resistance due to tissue ionic charges and fat content may retard the diffusion and/or effectiveness of EDTA, thereby prolonging the decalcification process.

To improve the tissue’s internal limitations, in this paper, we propose a novel permeability-enhanced method by the addition of detergents and NaCl to the EDTA-based decalcifier. The detergents are supposed to remove the hydrophobic elements or block lipophilic groups around the ion passages in tissues, and the hypertonic saline might mask the electric charges so that the polar molecules would penetrate the tissues with fewer disturbances, allowing for rapid permeation of the water soluble agent EDTA.

To test the hypothesis, we adopted three strategies: the first was to optimize appropriate concentrations of detergents and saline in a decalcified bone-mimicking substance—gelatin gel, based on penetration rate. To determine whether the optimal agents affect the reaction of EDTA-based decalcification, we performed eggshell experiments. Using the optimal EDTA and additives, the second was to evaluate the decalcification rate in bone tissue, judged by macro-Alizarin stain, under different conditions. To check for feasibility under different important conditions, we repeated the experiment in a varying reagent concentration and temperatures. Finally, the third was to assess the effects on the quality of tissue morphology, antigenicity, and nuclear acids, following decalcification by the new method.

EDTA permeability in gelatin gel

Bone mainly consists of collagens and inorganic minerals, and the high permeability of EDTA in the collagens is essential for the speedy decalcification of bone. To estimate the speed of the penetration of EDTA in the collagen of bone, we prepared collagen-rich gelatin gel (w/v, 4%) in diluted Weigert hematoxylin (Weigert A + B, see " Materials and methods " Section) and added EDTA or the mixture solutions on the casted gel in tubes. Prior to and after the addition of the EDTA, the penetration depth was measured at various time intervals including 15 min, 1 h, 2 h, 4 h, and/or 6 h. This was done by measuring the thickness of the light brown color in the gel, as shown in Fig.  1 , Figs.  S1 , S4 – S6 .

EDTA penetration in gel at different time points (0–4 h). 26% EDTA penetration in the gelatin gel ( A ) or in the fat-containing gelatin gel ( B ) varied with the addition of 5% NaCl (EDTA + sa) or 0.5% Tween/1% triton (EDTA + de), or 5% NaCl and 0.5%Tween/1% triton (EDTA-plus). PBSx1 served as a control. The penetration depth was measured in triplicate at each time point. Comparison of the average values among the experimental groups within the period 15`–4 h, with one-way ANOVA on ranks and Tukey HSD, resulted in a statistically significant difference; *for all, p < 0.002, vs 26% EDTA, 26% EDTA + sa, or 26% EDTA + de; N = 4 (4 average values for 4-time points per group) in the gel without fat ( C ); **for all, p < 0.001, vs. 26% EDTA, 26% EDTA + sa, or 26% EDTA + de; N = 4 in the fat-containing gel ( D ). At the first (15 min) and final (4 h) time points after the addition of EDTA, the penetration depths (N = 3 per group) were compared among the experimental groups, with one-way ANOVA and Tukey HSD. The p values of the pairwise comparisons were given in Tables C1 (15 min) and C2 (4 h) for graph C, and Tables D1 (15 min) and D2 (4 h) for graph ( D ). In the tables statistically significant differences were marked in boldface. Green bars in ( A ) = 10 mm. Blue bars in ( B ) = 20 mm.

The principle behind the measurements is that the dark-colored Weigert hematoxylin (Weigert A mixed with Weigert B) in the gel became light brown when contacted with EDTA (refer to Fig. S3 for more details).

To optimize the concentration of saline and detergents—the potential enhancers for EDTA-based decalcification, multiple concentrations of NaCl and detergents were prepared with 15% EDTA, and EDTA penetration depths were measured at several time points (see Fig.  1 , Figs.  S4 – S6 ). Based on the penetration depth, the optimal concentrations for saline and detergents were 5%, and a combination of 0.5% tween-20 and 1% triton-X100, respectively (see Figs. S4 , S5 ), which had the fastest rate of diffusion of EDTA in its group. It should be stated that 10% saline gave out an equal effect to 5% saline (Fig. S4 ) but was excluded from the list due to being just partially dissolvable during the subsequent preparation of a mixture of highly concentrated detergents. To search for the possibility of additivity of saline-detergent mixture on the EDTA diffusion, in another further experiment (Fig. S6 ), we prepared a mixture of the optimized concentrations of saline and detergents (5% saline and 0.5%Tween 20/1% Triton X-100 prepared in 15% EDTA, and mixed,15% EDTA-plus) and found that the mix was superior to either the optimized concentrated saline or detergent group alone in the penetration rate.

To test the effects of the additives on the penetration of different concentrations of EDTA, we prepared 26% EDTA (w/v) and its mixtures with the optimized concentrated saline (26% EDTA + sa), detergents (26% EDTA + de), or both (26% EDTA-plus), respectively. We mounted the solutions on the gels, and measurements of EDTA penetration at room temperature were performed at different time points (see Fig.  1 A). As a result, we found that 26% EDTA-plus induced the fastest penetration of EDTA among the groups (Fig.  1 A,C). The result was further confirmed in repeat experiments and there were statistically significant differences, in comparison of the average values among groups, with one-way ANOVA on ranks followed by Tukey HSD (Tukey's Honestly Significant Difference test) (see Fig.  1 C).

From the results, we can speculate that the range of NaCl concentrations above 5% might potentially cancel the ionic interaction of the collagen-mimicking gel, and the mix of 0.5%Tween 20 and 1% Triton X-100 may weaken the action of lipophilic groups of amino acids in the gel. Thus EDTA—the polar molecule would go through the passage of the gel with fewer resistances, leading to a higher penetration rate.

For a study of detergent diffusion into fat-containing tissues, we prepared gelatin gels with the addition of fat extracted from a mouse liver. 26% EDTA and the mixtures mentioned above were added to the gels and the penetration depth was measured at each time point and the analysis is shown in Fig.  1 B. The overall outcomes showed that the addition of fat in the gel slowed down the EDTA penetration in EDTA and saline-containing EDTA groups (see Fig.  1 B,D) with respect to the EDTA penetration of the same groups in the gel without the addition of fat at each time point (see Fig.  1 A,C), suggesting that the fat may retard EDTA diffusion. However, the detergent-containing EDTA, inclusive of 26% EDTA-plus and 26% EDTA + de groups (see Fig.  1 B,D), exerted their pronounced effects on penetration in comparison with the gel of non-detergent-contained groups, implying that the detergents may remove the fat and help enhance EDTA diffusion (see Fig.  1 B,D).

In conclusion, EDTA containing the additives of hypertonic saline or detergents assists in rapid penetration through the collagen-mimicking gel maybe by masking or delisting the charges or lipophilic elements which hinder the diffusion of EDTA, resulting in high permeability. 26% EDTA-plus exerts the strongest effects on the penetration rate in comparison with the counterparts.

Hypertonic saline and detergents do not affect the chelate reaction of EDTA

We have shown that both hypertonic saline and detergents can accelerate the penetration of EDTA in tissue-mimicking gels. The next issue that needs to be answered is whether the agents in such a hypertonic concentration affect the reaction of EDTA-based decalcification since normal chelate reactions require a proper microenvironment. To address the question, we prepared powder of chicken eggshell. Chicken eggshell is a composite material containing over 94% calcium salt, and thus is an ideal natural source for studying the decalcification reaction with minimal outside influence, such as diffusion.

The eggshell powder was incubated with 26% EDTA mixtures of hypertonic saline and/or detergent at room temperature (see Fig. S7 ). 26% EDTA alone and PBSx1 in parallel served as controls. The eggshell powder loss in weight after decalcification (difference of powder weight before and after decalcification incubation) was determined. The results showed a similar weight loss at each time point among the decalcification groups (Fig. S7 ) and nearly no weight loss among the time points for the PBSx1 group. The repeat experiments reflected a similar outcome and there was no statistically significant difference among the decalcification groups in terms of the average values for each time point. The results indicate that the addition of hypertonic saline and the detergents into the EDTA solution does not interfere with the decalcification reactions.

Quantification of decalcification in tibia of mouse

To evaluate the efficiency of the addition of hypertonic saline and detergents on EDTA decalcification of bone tissue, a group of mouse hind paws of similar weight in the same age, species, and sex (see Table S1 ) was skinned, fixed, and randomly submersed in solutions of 15% EDTA, 15% EDTA + sa (5% saline prepared in 15% EDTA), 15% EDTA + de (1% triton (v/v) and 0.5% tween (v/v) prepared in 15% EDTA), and 15% EDTA-plus (5% saline, 1% triton, and 0.5% tween prepared in 15% EDTA) for decalcification, and in PBSx1 for a control. Prior to and after being incubated at room temperature under continuous agitation for 6 h, 24 h, and 3 days, the specimens were collected and subjected to Alizarin staining. The mineral content of the tibia in the distal portion, indicated by the Alizarin-stained red color (see Fig.  2 A,B), was measured by image analysis. The mineral retaining in the relative area is the ratio between the mineral content area of red captured in a relatively long exposure light image and the sample contour area displayed in a relatively short exposure light image. The mineral loss in the relative area via decalcification was the value of the retained mineral in the relative area subtracted from 100%. The outcomes showed that the mineral loss via the decalcification with EDTA mixture either of saline or detergents or with saline and detergents is above EDTA used alone at each time point, and 15% EDTA-plus was proved to be the most efficient solution in decalcification in comparison with those of other counterpart solutions at a temperature (room temperature, RT or 45 °C) (see Fig.  2 C). As a control (PBSx1), the mineral content was kept unchanged. The subsequent repeated experiments demonstrated a similar result. Comparisons of average values among the groups, with one-way ANOVA on ranks, or comparisons of single values at each time point, with one-way ANOVA, followed by Fisher LSD test (Fisher’s protected Least Significant Difference Test), resulted in statistically significant differences (see Fig.  2 C and Table 1 ).

Mineral retention via EDTA decalcification of different methods in the distal portion of tibia. ( A ) The macroscopic stain of Alizarin following decalcification of 15% EDTA, 15% EDTA with additive of 5% saline (15% EDTA + sa), 15% EDTA with additive of detergent mixture of 0.5% tween-20 and 1% triton-X100 (15% EDTA + de), and 15% EDTA with additive of both saline and detergents in the same concentration (15% EDTA-plus) at room temperature (RT, 23 °C). A mixture of 0.5% tween-20, 1% triton-X100 and 5% NaCl (saline + deter) served as a control. The red color indicates the presence of calcium. ( B ) The region of interest (green rectangle) in the distal portion of the tibia was shown as an image (larger green rectangle). ( C , D ). The mineral loss in the relative area measured in triplicate or quadruplicate was compared among the groups. The comparison of the average values among the experimental groups within the period 6 h—3 days, with one-way ANOVA on ranks and Fisher LSD test, resulted in a statistically significant difference; *for all, p < 0.04, vs. 15% EDTA RT, 15% EDTA + sa RT, 15% EDTA + de RT, 15% EDTA-plus RT, or 15% EDTA 45 °C; N = 3 (3 average values for 3-time points per group) in ( C ); **for all, p < 0.001, vs 26% EDTA RT, 26% EDTA-plus RT, or 26% EDTA 45 °C; N = 3 in ( D ). ( E ) Comparison between the values of 15% EDTA-plus and 26% EDTA-plus at a higher temperature (45°C), measured at 6 h, with Paired Samples T-tests, led to a significant statistical difference; #, p < 0.01 of 2-tailed, N = 4. Green bars in ( A ) = 1 mm.

To test the feasibility of the application under different conditions, we focused on the decalcification efficiency of using a higher EDTA concentration of 26%, and of raising temperature to 45 °C (Fig.  2 D). Similar results to the use of 15% EDTA (Fig.  2 C) were observed (Fig.  2 D). A significant difference was found at 6 h with 26% EDTA-plus 45°C giving a more rapid decalcification rate (Fig.  2 D, Table 1 ).

The results demonstrated that a higher temperature of decalcification or a higher concentration of EDTA led to a greater efficiency of decalcification, compared to the other combinations tested (Fig. 2 C–E). When the optimal conditions found in Fig.  2 C,D were compared directly (Fig.  2 E), then the 26% EDTA-plus 45°C was significantly higher than the 15% EDTA-plus 45 °C at 6 h. 26% EDTA-plus at 45°C led to complete decalcification (100%) within 24 h but 15% EDTA-plus to 99.56% at the same temperature (Fig.  2 E). The results suggest that the addition of saline or/and detergents can enhance the efficiency of EDTA decalcification and significantly shorten the decalcification time in bone tissue.

Histological analysis of retention of tissue structure and cellular detail after decalcification

Morphological preservation after decalcification is a prerequisite for the application of the relevant stains. To inspect the potential impacts of a divergent fashion of decalcification on morphological retention, we prepared the right hind paws of a cohort of 12 male mice of a similar age, and body and hind paw weight (see Table S1 ). 10–15% EDTA at room temperature for decalcification is considered the “gold standard”—which is the technique most cited in the literature. Thus, the samples were randomly and equally divided into two groups. Six paws in one group objected to decalcification with 26% EDTA-plus at 45 °C under agitation for 24 h (26% EDTA-plus)—the most efficient decalcification method verified in our previous tests, and the rest 6 paws in the other group with 15% EDTA at room temperature under agitation (15% EDTA) for one week 35 . Then the samples underwent paraffin embedding before the sagittal sections of ankle joints were cut (Fig. S2 ).

To test the retention of morphological details following the decalcification, we performed (1) Hematoxylin and eosin (HE) for examination of general tissue structures and cellular details; (2) Safranin O (Saf O) and Toluidin Blue (TB) for inspection of proteoglycan status; and (3) Sirius Red (SR) for evaluation of collagen retention. The stains were performed in the middle sagittal sections of the ankle joints (Fig. S2 ). The outcomes of the stains of HE, Saf O, and TB were analyzed, with emphasis on the subperichondrial layer of distal articular cartilage of the tibia. The intensity of collagen stain was evaluated in the tibial cortical bone of the distal part (exclusive of bone marrow).

HE staining

HE-stained sections were observed using light microscopy. The observations showed that qualified sections, via 26% EDTA-plus (45 °C) decalcification, were well-preserved (see Fig.  3 A–D). There were no rips or tears in the sections. The specimen structures and components were kept intact, and the cell structures were clear in contrast with a clean background. No artifacts or detrimental structures were observed. The tissue structures and cellular details via 26% EDTA-plus (45 °C) were like those following the standard technique (see Fig.  3 A–D).

HE stained sections of ankle joints via decalcification of 26% EDTA-plus 45 °C and 15% EDTA RT. The regions indicated by blue rectangles in ( A – C ) were magnified in the images of ( B – D ), respectively. In terms of the relative shrinkage cells (%) in ( E ) and pyknotic nuclei (%) in ( F ), the comparison between 26% EDTA-plus 45 °C and 15% EDTA RT, with Independent Samples T-test, resulted in statistically non-significant differences (N = 6 per group; p = 0.54 of 2-tailed, for ( E ); P = 0.83 of 2-tailed, for ( F )).

For evaluation of cell shrinkage or pyknotic nuclei, which were likely to be produced at a higher temperature and in a hypertonic decalcification solution of 26% EDTA-plus (45 °C), we counted the positive cells or nuclei and all the cells or nuclei examined under a higher magnification (an × 40 objective lens). Light microscopy revealed no qualitative differences in comparison with the standard technique regarding cell shrinkage or pyknotic nuclei (%) and there were no statistically significant differences in comparisons with Independent Samples T-test (Fig.  3 E,F).

Pyknosis is defined as the “shrinkage of the nuclear material of a cell into a homogenous hyperchromatic mass” 16 . Shrinkage is defined as the separation between the chondrocyte membrane and the lacunar rim 16 . The shrinkageor pyknotic rate is the ratio of the positive cell or nucleus number to the total number counted in the distal articular cartilage of the tibia.

Saf O or TB staining

The stains are often used for the determination of proteoglycan loss and cartilage erosion in inflammatory arthritis. A reliable decalcification should not affect the stains of Saf O or TB. To evaluate the potential effects of the new decalcification method on the proteoglycan stains, we measured the cartilage thickness in the distal end of the tibia, following the stains, and made a comparison with the outcomes via the standard technique (see Figs. S8 , S9 ). The cartilage thickness for each joint was determined by the cartilage area divided by the cartilage surface length. A similar thickness was displayed to that via the standard technique. No staining difference was observed in histological sections between the two groups, suggesting that 26% EDTA-plus (45 °C) did not induce loss of proteoglycan in the tissue.

Sirius red staining

The staining is one of the best-understood techniques of collagen histochemistry. The collagen loss is reflected in the staining intensity. We measured the stained orange intensity in the tibial cortical bone with Image Analysis. The results showed that the staining intensity was similar between the two groups, suggesting EDTA-plus did not cause loss of collagen in the tissue (refer to Fig. S10 ). In the high magnification of the staining, we also found a similar collagen distribution.

Histochemical analysis of retention of enzyme activity after decalcification

Tartrate-resistant acid phosphatase (TRAP) staining is widely employed for the detection of multinucleated osteoclasts and scoring the extent of bone erosions. To determine whether the new decalcification technique would also accelerate quenching of TRAP enzyme activity, we performed the staining in sections adjacent to those stained with HE, Saf O, TB, or SR. The TRAP staining positive relative area was calculated by the ratio between the positive staining area and the tibia-involved area in the distal shaft region of the tibia. Comparison of the relative area, with Independent Samples T-test, in the tissues via 26% EDTA-plus (45 °C) and 15% EDTA (RT) led to no statistically significant difference (Fig. S11 ). The results suggest that the new decalcification technique did not affect enzymatic activities.

Immunohistochemistry analysis of retention of tissue antigenicity after decalcification

Tissue antigenicity including the specificity and sensitivity should not be affected by the processing of the new decalcification style. To evaluate the tissue antigenicity, we carried out immunostaining for inflammation-relevant cells via the new decalcification model. Inflammation is the most common sign in joint diseases and immunological detection for inflammatory cells is often used for evaluation of the severity of the diseases in the laboratory. To develop arthritis, male DBA/1 mice, 8 weeks in age, were immunized and boosted with bovine collagen II and human fibrinogen as previously reported 36 . After 12 weeks of immunization, the left hind paws, which weighed over 0.180 g (0.144–0.151 g of the native mice at the corresponding age, see Table S1 ), were randomly objected to decalcification either with 15% EDTA (RT) for 1 week or 26% EDTA-plus (45 °C) for 24 h as described. Following the decalcification, we carried out sectioning and HE staining as previously described. The adjacent sections of HE stained which displayed moderate inflammation, were selected for traditional ABC immunohistochemistry (IHC) for Macrophages, CD45, and CD3. To minimize bias due to various sites of observation, we focused on the pannus formation of arthritis in the ankle joints, limiting to invaded portions within the articular cavity.

No staining difference was observed in the histological sections via the new decalcification method, in comparison with corresponding staining via the standard technique, with respect to the targeted cell density (see Fig.  4 A,B). For evaluation of the cell density, we first defined the region of the pannus—the invasive part of thickened synovium into the articular cavity and measured the area of the region with Image analysis (see Fig.  4 B). In the region, we counted the positive cells in terms of blue nuclei. The positive cell density is the ratio of the positive cell number to the involved area as reported 42 , 43 . The experiments were validated by the findings of a parallel experiment on the spleen. As positive controls for all the targets, the spleen sections revealed positive, and blank controls, without application of the relevant antibodies, displayed negative.

Immunohistochemistry of sections of ankle joints following decalcification of 26% EDTA-plus (45°C) and 15% EDTA (RT). ( A ) Images of immunohistochemistry of ABC for macrophages, CD3, and CD45 were performed with a counterstain of hematoxylin. The positive cells (indicated by arrows) are colorized dark brown. Bars = 20 µm. ( B ) The region of interest (green circle) is localized in the pannus formation of arthritis. The thickened synovium and pannus formation is marked with red. ( C ) Inflammation cell density within the pannus of arthritis via decalcification of 26% EDTA-plus (45 °C) and 15% EDTA (RT) was demonstrated. In terms of the cell density, the comparison between them, with Independent Samples T-test, resulted in statistically non-significant differences (N = 5–6 per group; p = 0.07 of 2-tailed for macrophages; p = 0.13 of 2-tailed for CD3; p = 0.82 of 2-tailed, for CD45).

Nuclear staining and in situ hybrid analysis of retention of cellar DNA

Good retention of cellular nuclear acid after decalcification is very important for current molecular pathology. To evaluate the retention of quantity and quality, we did HE and DAPI fluorescence staining, and whole chromosome Y painting in the decalcified ankle joints with either 26% EDTA-plus (45 °C) or 15% EDTA (RT) as described previously. DAPI fluorescence staining and the chromosome Y painting were performed in the adjacent sections of HE stained. The joints were derived from a cohort of mice in a similar background (see Table S1 ).

We observed nuclear details of blue color in HE stains (Fig.  3 D) and those of blue fluorescence in DAPI staining (Fig. S12 ) under an × 40 objective lens. The stains looked similar in the tissues via both decalcification styles (Fig.  3 D, Fig.  S12 A). Comparison of the nuclear area per cell in DAPI staining resulted in no statistically significant differences (Fig. S12 B). To assess nuclear acid integrity, a mouse Whole Chromosome Y Painting FISH Probe was used to determine the copy number and integrity of complete mouse chromosome Y and detect the possibility of abnormalities of the chromosome. The painting was completed with counterstaining of DAPI (see Fig.  5 A). Blending the nuclear-stained blue and the chromosome Y-stained red fluorescence allowed for the identification of positive nuclei. We counted the positive nuclei of both red and blue, and the total nuclei of blue. The positive proportion to the total (positive rate) was calculated. Comparison of the positive rate resulted in a high closeness in the tissues via both decalcification styles (no significant statistical difference between them, see Fig.  5 B).

DAPI staining and in situ hybrid painting for Y chromosome of moue. ( A ) Images of the staining. The regions indicated by red rectangles in insets were magnified in the corresponding images and the regions of interest (white rectangles) were observed under DAPI and Cy-3 channels. Bar = 50 µm. ( B ) Y chromosome painting positive rates were calculated and compared in the tissues decalcified with 26% EDTA-plus 45 °C and 15% EDTA RT. The comparison between the two decalcification fashions, with Independent Samples T-test, resulted in a statistically non-significant difference (N = 6 per group, p = 0.33).

RNAscope technology for detection of preservation of gene expression (RNA)

Retention of RNA in decalcified tissue is critical for gene expression analysis in modern molecular biology. RNAscope is a novel and sensitive technology that can be used to measure single RNA molecules per cell in samples mounted on slides. To assess the retention of RNA, an RNAscope was employed and performed in the adjacent sections of HE stained. We selected beta-actin housekeeping genes as a preservation indicator of mRNA since housekeeping genes are commonly described as stably expressed irrespective of tissue type, developmental stage, cell cycle state, or external signal. We observed the main parts of red labeled dots located within one side of the cytoplasm and minor parts within nuclei (see Fig.  6 B). Each dot of red fluorescence represents one RNA molecule. However, it is noteworthy that in the case of the highly expressed housekeeping gene – ß-Actin, the dots were found in clusters, which makes them difficult to distinguish separately. We measured the integrated density in the middle chondrocyte region (Fig.  6 A) of the distal end of the tibia and compared the density of the samples via the two decalcification styles. Comparisons led to a statistically significant difference (see Fig.  6 C), which indicates that the decalcified samples via 26% EDTA-plus (45 °C) better preserve mRNA than those via 15% EDTA (RT) and the difference may be due to a shortened decalcification period of 26% EDTA-plus (45 °C).

Images of RNAscope and DAPI staining in chondrocytes of mice. The region indicated by the red rectangle in ( A ) was observed in images ( B ) under DAPI and Cy-3 channels. The regions of interest (white rectangles) in ( B ) were magnified in the adjacent corresponding images. Mouse ß-actin mRNA expression in chondrocyte (red fluorescence) was calculated in terms of integrated density and compared in the tissues decalcified with 26% EDTA-plus (45°C) and 15% EDTA (RT). The comparison ( C ) between the two decalcification fashions in the regions limited to the middle portion (100 µm) demarcated as the red rectangle of ( B ), with Independent Samples T-test, resulted in a statistically significant difference (*vs. 15% EDTA RT, N = 6 per group, p = 0.034).

Accuracy and comparison of staining outcomes via 26% EDTA-plus and 15% EDTA

Accuracy is the closeness of agreement between the tested results and accepted reference values 37 , 38 . Staining results with decalcification of 10–15% EDTA are widely used and accepted for morphological analysis. The degree of closeness between the outcomes via 26% EDTA-plus (45 °C) and 15% EDTA (RT) was tested by assessing the correlation. We pooled the average values determined for each kind of stain in the decalcified tissues with either 26% EDTA-plus (45 °C) or 15% EDTA (RT). The values were from 1. Cell shrinkage rate, 2. Nuclear pyknotic rate, 3. Macrophage, 4. CD3, 5. CD45, 6. DNA painting, 7. DAPI staining, 8. TRAP staining, 9. Saf O staining, 10. TB staining, 11. Sirius staining, and 12. RNAscope. Comparison of the outcomes resulted in a statistically significant correlation (see Fig.  7 ). The similarity in the morphological features generated via 26% EDTA-plus (45°C) to those with 15% EDTA (RT) confirms that staining outcomes with 26% EDTA-plus (45 °C) are accurate. Of note, an outlier (v1 in Fig.  7 ), located far away from the trendline (others), is from the corresponding average values of RNAscope. These results further partially confirm that the general relationship in the other categories becomes strong although decalcification via the new method yields more mRNA (discrepancy).

Accuracy testing on average values of each stain in tissues via decalcifications of 26% EDTA-plus (45 °C) and 15% EDTA (RT). The statistical analysis resulted in a statistically significant correlation (N = 12 pairs, Pearson’s r = 0.967, p < 0.001). V1 indicates the data point of RNAscope analysis.

In conclusions: decalcification with 26% EDTA-plus (26% EDTA with the addition of saline and detergents at 45 °C) is considerably accelerated and the speedy decalcification may act through a quick diffusion system by blocking and/or removing tissue intrinsic charges or fat. Thereafter 26% EDTA-plus (45 °C) provides appropriate results for histostaining, histochemistry, immunohistochemistry, and in situ hybrid painting, and better outcomes for mRNA preservation, in comparison with the standard technique. The results imply that 26% EDTA-plus (45 °C) is a valuable and quick decalcified method and may be recommended for urgent needs in calcified tissues.

Chelating agents such as EDTA work by capturing the calcium ions from the outer layer of the apatite crystal at the initial stage, and then the decalcification reaction occurs slowly in the interior of bone tissue. The reaction interface is originally on the surface of the tissue block, and with the advance of decalcification, the interface decreases in size. When decalcification is complete the reaction surface approaches zero. During the decalcification, the thickness of the organic layer (mainly collagen) of decalcified material between the reaction interface and the decalcifier increases, accumulatively impeding diffusion, namely, the interchange of a free supply and removal of the substances involved. The rate of decalcification (amount of calcium salts extracted per unit of time), therefore, will reduce with the development of the process. This is why the rate of decalcification accelerated very quickly at the beginning, but eventually slowed down considerably, particularly in the period of nearly 100% decalcification.

To accelerate the decalcification process, two factors can be manipulated, namely, by increasing the rate of reaction and by increasing the rate of diffusion, respectively. For the rate of reaction, the decalcifying agent—EDTA, and the target tissue—bone have been designated in the case, and accordingly, we can do nothing to interfere with this factor. This has been partially confirmed by the tests on the chicken eggshell (see Fig. S7 ). However, the diffusion factor can be made to use in any attempt to influence the rate of decalcification. Diffusion is the passive movement of substances from a region of higher concentration to a region of lower one. The rate of diffusion is affected by the concentration gradient, the reaction interface area, the thickness between the reaction interface and the decalcifier, tissue permeability, and temperature. The outside physical factors, such as temperature, EDTA concentration, agitation, ultrasound, irradiation of microwave, etc., have been widely reported. By purely mechanical means, diffusion of the agents was enhanced inside the tissue, and decalcification was accelerated. To increase the reaction interface area and shorten the thickness between the reaction interface and the decalcifier, lots of experiments have been displayed in previous reports with sawing a large bone into small thin logs before decalcification. However, tissue internal chemical factors concerning the tissue's intrinsic permeability have been neglected to some extent. The internal chemical factors that influence diffusion inside the organic material, including hydrophobic elements (e.g., fat) or lipophilic groups of amino acids, and the electric charges around the chemical ion passages in tissue.

Therefore, we hypothesize that the addition of hypertonic saline and detergents to the EDTA solution enhances the process of decalcification by accelerating tissue internal diffusion of relevant chemicals. The hydrophobic elements or lipophilic groups and the electric charges around the chemical ion passages in tissues might be removed and masked with detergents and hypertonic saline so that the polar molecules of the agents would penetrate the tissues with fewer disturbances, allowing for rapid permeation of the water soluble agent—EDTA (see Fig.  8 ). Thus, the concerning reagent transfer between the tissue to be decalcified and EDTA solution was facilitated when the detergents and hypertonic saline were present. Therefore, EDTA diffusion driven by the gradient concentration within the decalcified portion of tissue was accelerated, and decalcification became faster. The possible principle of this work is shown in Fig.  8 .

Fundamentals for accelerating diffusion of EDTA in tissue. ( A ) Proteins are cross-linked by induction of formalin-fixation and charged while contacted with an EDTA solution. Lipid droplets are inset amid the proteins. The charges and lipids impede EDTA penetration into the tissue. ( B ) EDTA penetration is enhanced after delipidation with detergents. ( C ) An additive of NaCl in the EDTA solution, shielding the charges around the penetration passages, accelerates further the tissue permeability of EDTA. ( D ) Symbol identifiers for ( A – C ).

For evaluation of the assumption, we determined the depth of EDTA infiltration in the tissue-mimicking gelatin (collagen) gel and the rate of decalcification in the ankle joints of mice. The tests on the collagen-rich gelatin gel have verified that detergents and hypertonic saline aided in the quick decalcification maybe by the speedy diffusion or permeation of EDTA. The higher the content of fat the gel contains, the more activity the detergent containing EDTA displays (Fig.  1 ). The assumption was further verified in the experiments of decalcification of the mouse paws (Fig.  2 ).

With the decalcification of the EDTA mixture of detergents and hypertonic saline, good morphological retention was demonstrated in the histological stains, TRAP, immunohistochemistry and in situ hybrid painting (Figs.  3 , 4 , 5 , Figs.  S8 – S12 ), and mRNA was better preserved (Fig.  6 ) maybe due to the shorten decalcification. The outcomes via 26% EDTA-plus (45 °C) were equal or superior to those via the standard technique in tissue component retention (Fig.  7 ).

This is an important issue, especially in the field of surgical pathology, where minimal incubation duration, good preservation of morphology and target antigenicity, and good retention of nuclear acid integrity are always required. Thus the technique would benefit the laboratory by extending its application in urgent circumstances and increasing the staining qualities of the specimen. For example, in clinics, bone or bone marrow biopsies are always urgently needed for the diagnosis of hematologic cancer, metastatic tumors, and primary bone sarcoma.

The chemicals used are cheap and easily accessed. The detergents and saline are routinely employed for delipidation and solution osmotic balance for preparation of histological stains, immunohistochemistry, and in situ hybrid in the laboratory. The procedure in the new decalcification way is simple and consistent with the diagnostic workflow currently used by pathologists. It has the potential to become a routine method for decalcification in the laboratory of histopathology.

For a successful application of the method, five universal points should be paid attention to, (1) Volume and concentration of decalcifying solutions. The higher concentration of the active agents will increase the rate at which calcium is removed from the bone to be decalcified. It must be remembered that the concentration of the active agent will be depleted as it combines with calcium so it is wise to use a large volume of the decalcifier or renew it several times during the decalcification process. (2) Temperature. Increased temperature will speed up the decalcification rate but will also increase the rate of tissue damage. Therefore, the temperature must be carefully controlled 39 . A working temperature of 30–45 °C is recommended, at which the decalcification would be accelerated in the EDTA mixture efficiently and histological and immunohistochemical analysis would not be affected. (3) Agitation. Strong agitation increases the rate by enhancing the active agent diffusion. (4) Fresh decalcifier. Freshly prepared decalcifying solutions have ready access to all surfaces of the specimen. This will enhance diffusion and penetration into the specimen and facilitate ionization and removal of calcium. (5) Decalcification duration. Using the 26% EDTA-plus at 45 °C, 24 h-decalcification worked well for adult mouse ankle joints. For the bone tissues derived from mice of a divergent age or other species of animals, optimization of decalcification should be performed for the duration.

Despite successfully speeding up the decalcification, at least three weaknesses should be paid attention to when using the current technique. (1) Application of this technique should be avoided in the tissues with an intention for other heavy metal detection (e.g., Fe, Cu, etc.) or the tissue block staining involving heavy metals (e.g., Fe in tissue block stain with Weigert Hematoxylin) since the method would extract both calcium and other heavy metals from the tissues or the dye of the stain. (2) The technique is not recommended for use in the tissue to be decalcified for lipid measurements or staining because the lipids in the tissue tend to redistribute or lose after detergent permeabilization of the decalcification solution. (3) Other salts or detergents, which may also increase the efficiency of decalcification, were not involved in the work and should be optimized for the best concentration and processing if applied.

Materials and methods

This work, inclusive of tissue processing, cutting, staining, gel-relevant experiments, etc. was completed in a laboratory with a room temperature of 23 °C, except as stated.

Gel preparation

4% gelatin gel (w/v) (Cat# 4274.1, Carl Roth, Germany) in distil water was prepared by heating using a microwave. After cooling to 55–60 °C, the melted gel was added with labeling chemicals (Weigert A/B mixture at a ratio of 1:1, Cat# X906.1 and X907.1, Carl Roth, Germany) in a volumetric ratio of 5:1. Take 3 ml of the melted labeling gel into a 15-ml tube, cast the gels in 4 °C for 30 min, and balance the tube at room temperature for 30 min before EDTA (Cat# CN06.4, Carl Roth, Germany) or the mixture addition (for more details, refer to Table S2 ). For the preparation of fat-contained gelatin gel, add ~ 0.1 g of lipids extracted from a liver (to be described down) into a 15-ml melted labeling gel and mix well before distributing 3 ml of the gel to a tube and casting.

Lipids extracted from liver

We used Folch’s extraction procedure 40 for isolating lipids from a liver. It takes advantage of the biphasic solvent system consisting of chloroform/methanol/water in a volumetric ratio of 8:4:3. For more details, refer to Table S2 . With the method, we yielded fats of 0.29 g from a liver weighing 1.34 g. The liver was derived from an 8-week-old male C57BL/6J mouse.

Preparation of eggshell powder and decalcification

Bavaria cracked chicken eggshells from Germany were collected and washed. The dry-washed eggshells were crushed and ground in a Pulver (Cell crusher, Cat# 538,003, Kiskerbiotech GmbH and Co KG, Germany). After the powder was sieved with a sifter (Ø, 1 mm), the sieved pellets were washed with distil water and dried completely in an oven. Then a decalcification reaction was carried out by mixing 0.5 g of the chicken eggshell granule with 45 ml of 26% EDTA (pH = 7.4) or 26% EDTA mixtures (pH = 7.4). The 26% EDTA mixtures include 26% EDTA containing 5% Saline (26% EDTA + sa), 26% EDTA containing 0.5% Tween-20 (Cat# 9127.1, Carl Roth, Germany), and 1%Triton-X100 (Cat# 3051.3, Carl Roth, Germany) (26% EDTA + de), and 26% EDTA-plus (Fig. S7 ). The same volume of PBSx1 mixed with 0.5 g of eggshell granule served as a control. For more details, refer to Table S2 . At the ends of 30 min, 1 h, and 3 h after incubation, the decalcification reaction under agitation was stopped to measure the weight of the remaining eggshell pellet.

Animals and specimen preparations

This study was approved by the Regional Ethical Committee and the Government of Upper Bavaria, reference number: ROB-55.2–2532.Vet_02-19–69 and ROB-55.2–2532.Vet_02-16–115, based on a certified biostatistician's prior evaluation of animal study plan design and group sizes. All protocols regarding animal handling and experiments comply with Directive 2010/63/EU and the NIH Guidelines, and were reported in accordance with ARRIVE guidelines 41 .

Three cohorts of mice were used for this work and summarized in Table S1 on the strain, gender, age, body and hind paw weight, and source. These mice were euthanized in deep anaesthesia (Ketamin 150 mg/kg and Xylazin 15 mg/kg) at piloted time points and examined post-mortem for macro-anatomically pathological changes. The left or right hind paws were subsequently amputated at the level immediately above the external malleolus. The excised paws were weighed with a Sartorius fine balance (As-wägetechnik, Germany), skinned, and fixed in 4% (w/v) paraformaldehyde at 4 °C overnight. Decalcification was then performed under constant agitation for either subsequently piloted decalcification evaluation or the following paraffin embedding and histological/immunohistochemical stains (see the column of “purposes” in Table S1 ).

For the decalcification evaluation, 96 hind paws of male DBA/1 mice, 20 weeks in age, were randomly distributed to 11 groups (see Table S1 ) with 3—10 paws each. These paws were immersed in a volume of a solution of 200 times the samples (e.g., 1 g in 200 ml) for decalcification.

To evaluate the effects of the additives on decalcification of 15% EDTA at 23 °C, the solutions (see Table S1 ) prepared are 15% (w/v, pH = 7.4) EDTA, 15% EDTA containing 5% (w/v) NaCl (15% EDTA + sa), 15% EDTA containing 1% (v/v) triton-X100 and 0.5% (v/v) tween-20 (15% EDTA + de), 15% EDTA containing 5% saline, 1% triton-X100 and 0.5% tween-20 (15% EDTA-plus), and 5% saline containing 1% triton-X100 and 0.5% tween-20 (saline + deter). The decalcification with incubation in the solutions at room temperature (23°C) was stopped at the ends of 6 h, 24 h, and 3 days after the incubation. Three or four samples for each decalcification solution and time point were taken out for macro alizarin red staining to measure the remaining mineral of the distal portion of the tibia after decalcification. As controls, three samples in “15% EDTA RT” and “saline + deter” groups were picked out before decalcification and 24 h after the decalcification, respectively, for the macro alizarin red staining (refer to Fig.  2 , Tables S1 , S4 ). To assess the decalcification efficiency at a higher temperature we repeated the experiment for decalcification at 45 °C (Table S1 and Fig.  2 ).

In the same way, we also prepared 26% (w/v) EDTA, and 26% EDTA containing 5% saline, 1% triton-X100, and 0.5% tween-20 (26% EDTA-plus) and decalcified the samples at 23 °C or 45 °C to assess the decalcification efficiency (refer to Table S1 & Fig.  2 ) in a higher concentration of EDTA and its mixtures.

For assessment of morphological details following the decalcification, 12 right hind paws of C57 BL76J male mice, ranging 8.5—10 weeks in age, were randomly divided into two groups with 6 paws each (see Table S1 ). For the estimation of antigenicity preservation (see Table S1 ) after decalcification, we selected 12 left hind paws over 0.18 g in weight, from 20-week-old DBA/1 mice which had been immunized and boosted with bovine collagen II and human fibrinogen 36 . The 12 immunized paws were randomly and equally divided into two groups. One group objected to the decalcification of 26% EDTA-plus (45 °C) for 24 h (see Table S1 ). As controls, the other group was decalcified with the standard technique—15% EDTA (RT) for 1 week 35 . Following the decalcification, the samples were ready for paraffin embedding and sectioning.

Paraffin embedding, sectioning, and collection

Following decalcification, tissues were trimmed, washed in running tap water, and soaked in deionized water for 30 min each. Then the specimens were processed for dehydration in ethanol and clearing in xylol, before infiltration and embedding in molten paraffin wax at 65 °C. For more details, refer to Table S3 . For embedding, the samples were oriented with the external malleolus down for the right paw or the internal malleolus down for the left paw so that the soles of the paws were perpendicular to the cutting face.

After paraffin embedding, sagittal sections were cut at 5 μm with a Slee Cut 5062 rotary microtome (Slee Medical GmbH, Nieder-Olm, Germany). Paraffin ribbons at the middle of the ankle and tarsal joints were flattened in a water bath at 40 °C and collected onto polylysine microscope slides (Thermo Scientific) before drying at 45 °C overnight. For more details, refer to Fig. S2 in the Supplementary information. The paraffin sections were ready for histological staining.

Histological staining

Before staining, paraffin sections from C57 BL/6J and immunized DBA/1 mice were deparaffinized with xylene (2 times for 5 min at RT), followed by rehydration through a graded series of 100%, 96%, and 70% ethanol, and finally with distilled water (each 3 min at RT). Morphological preservation was evaluated by hematoxylin (Harris hematoxylin, Cat# HHS80, Sigma) and eosin (HE), Safranin O (Saf O, Cat# T129.1, Carl Roth, Germany), toluidine blue (TB, Cat# 198,161-5G, Sigma), Sirius red, TRAP, and 4, 6-diamidino-2-phenylindole (DAPI, Mounting medium with DAPI, Cat# H-1200, Vector Lab.) staining, in situ hybrid painting (WCP probe for mouse Chromosome Y, Cat# FPRPR0168, ASI-Applied Spectral Imaging), and RNAScope in the sections of C57 BL/6J mice. Tissue antigenicity retention was assessed with immunostaining in the sections of immunized DBA/1 mice. The stains mentioned are the most frequently used method in histological laboratories. For the staining procedures in more detail, see Table S4 in Supplementary information.

Immunohistochemical staining (IHC)

IHC was performed according to an established protocol 42 , 43 . Briefly, after deparaffinizing and rehydration, the sections were quenched in 1% hydrogen peroxide for 20 min and then submerged in 10 mM citrate buffer (pH 6.0) containing 0.05% tween-20 for 10 min at 85 °C and followed by cooling down to RT for 30 min for antigen retrieval. A blocking mixture of 2% mouse serum and 1% BSA was applied for 20 min to block the nonspecific binding. The sections were then incubated with preliminary antibodies (Table 2 ) for 30 min after tipping off the blocking solution, washed in PBS, and incubated with a biotin-conjugated anti-rat IgG antibody for 30 min. Following washing in PBS, incubation with streptavidin-conjugated HRP for 20 min was performed. Colorization was developed using 3,3-diaminobenzidine tetrahydrochloride (liquid DAB + Substrate DAKO). These sections were counterstained in Harris hematoxylin and examined under a light microscope (Carl Zeiss AG, Oberkochen, Germany).

For the validation of the experiments, a parallel experiment on the sections of the spleen, which served as positive or blank controls, was carried out.

RNAscope was performed according to the manufacturer’s protocol with minor modifications. The paraffin sections were deparaffinized and rehydrated as described above. The RNAscope pretreatment included incubation in 3% hydrogen peroxide for 10 min at room temperature, tissue retrieval by boiling for 15 min in RNAscope Target Retrieval Reagent solution to undo the cross-linking and treatment with RNAscope Protease Plus at 40 °C for 20 min. After the pretreatment, the sections were hybridized with RNAscope Probe targeting mouse Actin (Cat # 316,741, ACD, Farmington, UT, USA) at 40 °C for 2 h. The signals were amplified, detected with RNAscope 2.5 HD assay-RED kit (Cat # 322,360, ACD, Farmington, UT, USA), and counterstained with DAPI mounting medium, where the fast red can be visualized in the red fluorescent channel under a Zeiss microscope described down. The RNAscope negative control probe-DapB (cat. no. 310043, ACD, Farmington, UT, USA) was used as the negative control.

Alizarin gross staining

The decalcification degree was assessed by Alizarin stain at each time point (see Fig.  2 ) to determine the amount of mineral remaining with each decalcification method. After post-fixation in 90% ethanol and acetone, the samples were subjected to Alizarin staining until the bones became purple. Then the stained samples were ready for photographing followed by immersing in 50% glycerol prepared in distil water (for more details, see Table S4 ).

Image acquisition and histological analysis

Images were acquired under the bright field illumination on a Zeiss upright microscope and imaging system, and recorded with a 2560 × 1920 pixel resolution and JPEG mode. For fluorescence-stained sections, blue and red fluorescence signals were inspected and photographed in DAPI and Cy-3 fluorescence channels, respectively (DAPI: 359 nm excitation and 457 nm emission; Cy-3: 546 nm excitation and 568 nm emission).

With the 40 × objective lens, HE, DAPI staining, in situ hybrid painting, and RNAscope were inspected and photographed. The HE-stained sections were examined for shrinkageand pyknotic cells. The DAPI-labeled nucleus area was determined by image analysis (Adobe Photoshop V5), and the total nuclei and the painting-stained nuclei were counted. The average nuclear area per nucleus (µm 2 /nucleus) was determined by the ratio between the total DAPI staining area and the total nuclear number. The painting’s positive nucleus rate was the ratio of the stained number to the total nucleus number. The positive signal integrated intensity of RNAscope was measured with ImageJ.

Using the 20 × objective lens, the positive cells and the areas involved were counted in the immunohistochemical stained sections and determined by the Image analysis mentioned 42 , 43 .

Under the 10 × objective lens of the microscope, the targeted areas in Sirius red, TRAP stained, and Saf O and TB stained sections were acquired and measured with image analysis.

By the 2.5 × objective lens, the Alizarin-stained samples were examined and focused on the region of tibia and then imaged at both low (5 µs) and high (500 µs) exposures for each sample. The high-exposure images were used to determine the amount of mineral remaining reflected by Alizarin staining (Fig.  2 ), and the low-exposure images the whole outline area of the samples. The relative area was calculated by the ratio of the mineral remaining area to the outline area. The images were acquired under a lighting condition except for the exposure time mentioned above and focusing on each new visual field. The lighting condition includes color cold, 0.3; color saturation,—0.2; light strength,—0.24; and color contrast, -0.48.

Qualitative assessment of mineral retention after decalcification was undertaken with macro-Alizarin stain, allowing for determination of the mineral loss at time points for each solution. The mineral loss in the relative area is 100% minus the relative area mentioned above.

In addition, the gelatin gel running in the tube for penetration speed detection was macroscopically registered with a Canon camera. The penetration depth from up down was measured and calculated as described in Fig. S1 at each time point.

Data analysis

Data were presented as means ± SD. The software SPSS (IBM Corp. IBM SPSS Statistics for Windows, Version 11.0., USA) was employed for the analysis of correlation, Independent Samples T-test, one-way ANOVA, One-way ANOVA on ranks, or Paired Samples T-tests. One-way ANOVA on ranks was mainly used to identify differences in EDTA penetration in gel (for more details, see Method S1 in Supplementary Information) and EDTA efficiency of decalcification in tibias. A p < 0.05 was considered statistical significance.

Data availability

All relevant data are within the manuscript and its Supplementary information files.

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Acknowledgements

We acknowledge the excellent technical assistance of Ulrike Finger.

Support was provided by Advancecor GmbH of Germany, in the form of authors’ salaries and/or research materials. The research materials were the parts of the projects ongoing (reference number: ROB-55.2–2532.Vet_02-19–69 and ROB-55.2–2532.Vet_02-16–115). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.

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Zhongmin Li: Conception, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing. Clara Wenhart: Investigation & Resources. Andreas Reimann: Investigation & Resources. Yi-Li Cho: Writing – review & revising. Kristin Adler: Statistics & Revising. Goetz Muench: Project administration, Supervision.

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Li, Z., Wenhart, C., Reimann, A. et al. Hypertonic saline- and detergent-accelerated EDTA-based decalcification better preserves mRNA of bones. Sci Rep 14 , 10888 (2024). https://doi.org/10.1038/s41598-024-61459-8

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DOI : https://doi.org/10.1038/s41598-024-61459-8

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