hypothesis testing in software development

Hypothesis Testing: Driving Quality and Innovation in Software Development

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What is the Product Hypothesis and its Components?

A product hypothesis is a supposition that a certain improvement or modification in a product will enhance essential metrics such as revenue or user engagement. This hypothesis is vital in the field of software development and application design. A comprehensive product hypothesis typically consists of two crucial parts: the assumption and the prediction. The assumption lays the groundwork for the changes being considered, while the prediction anticipates the results of implementing these changes.

Understanding Hypothesis Testing

Today, the average user’s smartphone likely contains a myriad of apps, each frequently updated to provide the best user experience. Likewise, visiting an e-commerce website after just a few weeks often reveals changes in layout, user reviews, or available products. These updates are the result of a modern software development approach that involves releasing software in small increments, allowing developers to test assumptions and validate their product hypotheses. This methodology aims to constantly improve the product based on user feedback. Therefore, when a new feature is introduced to users, it’s crucial to validate any assumptions made about its design and functionality by assessing its real-world impact. This validation process is typically carried out through product hypothesis testing, where a hypothesis about the proposed change is formulated and then success criteria are established.

Hypothesis Testing Examples

To illustrate, let’s consider a software company that plans to implement a new feature in their product. The company could formulate a hypothesis that this new feature will increase user engagement. To test this hypothesis, they could conduct an A/B test, wherein the new feature is released to a subset of users while being withheld from others. Comparing user engagement between these two groups would then inform the company whether the new feature actually delivers the predicted value. For instance, a data product manager at Walmart might hypothesize that enlarging product images will boost conversion rates. Success, in this case, would be indicated by a measurable increase in these rates.

The Importance of Hypothesis Testing

Contrary to traditional methods of product design that involve lengthy development periods before release, modern approaches favor frequent, smaller changes that continuously deliver value to users. Hypothesis testing plays a key role in this process, as each assumption about a potential feature can be validated through targeted tests. This approach not only enables the product team to receive continuous feedback from users but also empowers them to course-correct as necessary, thereby maximizing the overall value of the product. Conducting and testing hypotheses every few weeks is often a more cost-effective and efficient way of creating a valuable product.

Different Types of Hypothesis Testing

• A/B Testing : A/B testing is a statistical method used to compare two versions of a software application or a webpage to ascertain which performs better. In this process, one group of users is shown one version of the software, while another group is presented a different version. Statistical analysis of user interaction data then informs which version delivers better results.
• Multivariate Testing : Unlike A/B testing, which considers only two variations, multivariate testing involves several versions of the product, each differing in certain elements such as content, image size, or color theme. Each variation is shown to a different user group, and their performance is compared to determine the most effective version. Despite requiring complex statistical analysis, this method provides a comprehensive understanding of multiple variables simultaneously.
• Before/After Testing : This method measures the impact of a change on a product’s performance by comparing metrics before and after its implementation. For example, a company might use before/after testing to evaluate the impact of a website redesign on user engagement or conversion rates.
• Time-based On/Off Testing : Ideal for systems that run continuously for extended periods, such as servers, this testing method involves running the system for a specific duration, shutting it down for a while, and then turning it back on. By simulating real-world events, this process can uncover and address any issues that may arise due to repeated system startups and shutdowns.

Application of Hypothesis Testing in Software Testing

Hypothesis testing is fundamental to software testing as it enables an evidence-based evaluation of new features or changes. For instance, if a team hypothesizes that a new feature will enhance system performance, they can test this feature and employ statistical techniques to analyze whether the data supports or contradicts their hypothesis. Hypothesis testing also aids in uncovering defects in specific modules or parts of the application, guiding testers in implementing data-driven tests. Consequently, stakeholders can make informed decisions about the product, bolstering overall product quality and user satisfaction.

Statistical Hypothesis Testing in Detail

In statistical hypothesis testing, two opposing hypotheses are created: the Null Hypothesis (H0) and the Alternative Hypothesis (H1). The Null Hypothesis is the assumption that the proposed change will have no effect on the outcome. In contrast, the Alternative Hypothesis suggests the proposed change will impact the outcome. A significant aspect of hypothesis testing involves avoiding Type I and Type II errors – incorrectly rejecting a true null hypothesis and failing to reject a false null hypothesis, respectively. A careful determination of significance level and interpretation of the p-value are also crucial to hypothesis testing. These statistical parameters guide the decision-making process, determining whether to accept or reject the null hypothesis.

Specific Use Cases

For instance, Facebook employs hypothesis testing when introducing new features to ensure they will improve user engagement. Similarly, Amazon uses it to optimize their recommendation algorithms, aiming to increase sales. In the realm of quality assurance (QA), tools like testRigor employ hypothesis testing methods to ensure the robustness and reliability of software applications. As a no-code test automation tool, testRigor uses its advanced AI engine to execute comprehensive testing, thereby minimizing defect escape rates and enhancing user experience.

Ethics of Hypothesis Testing

Hypothesis testing requires careful ethical considerations. Given that testing often involves user data, it’s crucial to respect privacy rights and adhere to all relevant data protection regulations. Moreover, ensuring the diversity of test groups is necessary to avoid skewed results. Hypothesis testing must also be designed to minimize potential biases, ensuring that the outcomes are as objective as possible.

Limitations of Hypothesis Testing

While hypothesis testing is a powerful tool, it’s essential to understand its limitations. For instance, while A/B testing can provide valuable insights, it might not capture long-term effects or external variables influencing user behavior. Moreover, multivariate testing, despite its comprehensive nature, can require complex statistical analysis and substantial data to yield valid results.

Implementation Strategies

Formulating a solid hypothesis, choosing the right type of hypothesis testing, avoiding common pitfalls, and correctly interpreting and acting on the results are all essential steps in a successful hypothesis testing process. For instance, tools like testRigor can assist in implementing these strategies, providing an intuitive, codeless platform for conducting comprehensive software tests. The use of plain English test statements simplifies test creation and execution, while its advanced AI engine ensures minimal maintenance. This makes testRigor an excellent solution for organizations seeking to streamline their testing processes and enhance software quality.

Relation to Machine Learning and AI

Hypothesis testing is also a crucial aspect of machine learning and AI. For example, AI models often rely on hypothesis testing to validate their predictions and enhance their learning mechanisms. In automated testing environments, platforms like testRigor utilize AI to create and execute tests, facilitating a thorough evaluation of software applications. Furthermore, AI can assist in identifying biases in AI and machine learning models, enhancing their fairness and reliability.

Future Trends

As machine learning and AI continue to advance, we can anticipate automated hypothesis testing to become more sophisticated. The integration of big data will enable more comprehensive hypothesis tests, uncovering intricate patterns and insights. Furthermore, integrating hypothesis testing into agile and DevOps practices can streamline software development processes, promoting continuous integration and delivery. Tools like testRigor are at the forefront of this evolution, utilizing AI to automate and enhance the testing process.

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Understanding Hypothesis Testing

Hypothesis testing involves formulating assumptions about population parameters based on sample statistics and rigorously evaluating these assumptions against empirical evidence. This article sheds light on the significance of hypothesis testing and the critical steps involved in the process.

What is Hypothesis Testing?

A hypothesis is an assumption or idea, specifically a statistical claim about an unknown population parameter. For example, a judge assumes a person is innocent and verifies this by reviewing evidence and hearing testimony before reaching a verdict.

Hypothesis testing is a statistical method that is used to make a statistical decision using experimental data. Hypothesis testing is basically an assumption that we make about a population parameter. It evaluates two mutually exclusive statements about a population to determine which statement is best supported by the sample data. 

To test the validity of the claim or assumption about the population parameter:

• A sample is drawn from the population and analyzed.
• The results of the analysis are used to decide whether the claim is true or not.

Example: You say an average height in the class is 30 or a boy is taller than a girl. All of these is an assumption that we are assuming, and we need some statistical way to prove these. We need some mathematical conclusion whatever we are assuming is true.

Defining Hypotheses

• Null hypothesis (H 0 ): In statistics, the null hypothesis is a general statement or default position that there is no relationship between two measured cases or no relationship among groups. In other words, it is a basic assumption or made based on the problem knowledge. Example : A company’s mean production is 50 units/per da H 0 : [Tex]\mu [/Tex] = 50.
• Alternative hypothesis (H 1 ): The alternative hypothesis is the hypothesis used in hypothesis testing that is contrary to the null hypothesis.  Example: A company’s production is not equal to 50 units/per day i.e. H 1 : [Tex]\mu [/Tex] [Tex]\ne [/Tex] 50.

Key Terms of Hypothesis Testing

• Level of significance : It refers to the degree of significance in which we accept or reject the null hypothesis. 100% accuracy is not possible for accepting a hypothesis, so we, therefore, select a level of significance that is usually 5%. This is normally denoted with  [Tex]\alpha[/Tex] and generally, it is 0.05 or 5%, which means your output should be 95% confident to give a similar kind of result in each sample.
• P-value: The P value , or calculated probability, is the probability of finding the observed/extreme results when the null hypothesis(H0) of a study-given problem is true. If your P-value is less than the chosen significance level then you reject the null hypothesis i.e. accept that your sample claims to support the alternative hypothesis.
• Test Statistic: The test statistic is a numerical value calculated from sample data during a hypothesis test, used to determine whether to reject the null hypothesis. It is compared to a critical value or p-value to make decisions about the statistical significance of the observed results.
• Critical value : The critical value in statistics is a threshold or cutoff point used to determine whether to reject the null hypothesis in a hypothesis test.
• Degrees of freedom: Degrees of freedom are associated with the variability or freedom one has in estimating a parameter. The degrees of freedom are related to the sample size and determine the shape.

Why do we use Hypothesis Testing?

Hypothesis testing is an important procedure in statistics. Hypothesis testing evaluates two mutually exclusive population statements to determine which statement is most supported by sample data. When we say that the findings are statistically significant, thanks to hypothesis testing. 

One-Tailed and Two-Tailed Test

One tailed test focuses on one direction, either greater than or less than a specified value. We use a one-tailed test when there is a clear directional expectation based on prior knowledge or theory. The critical region is located on only one side of the distribution curve. If the sample falls into this critical region, the null hypothesis is rejected in favor of the alternative hypothesis.

One-Tailed Test

There are two types of one-tailed test:

• Left-Tailed (Left-Sided) Test: The alternative hypothesis asserts that the true parameter value is less than the null hypothesis. Example: H 0 ​: [Tex]\mu \geq 50 [/Tex] and H 1 : [Tex]\mu < 50 [/Tex]
• Right-Tailed (Right-Sided) Test : The alternative hypothesis asserts that the true parameter value is greater than the null hypothesis. Example: H 0 : [Tex]\mu \leq50 [/Tex] and H 1 : [Tex]\mu > 50 [/Tex]

Two-Tailed Test

A two-tailed test considers both directions, greater than and less than a specified value.We use a two-tailed test when there is no specific directional expectation, and want to detect any significant difference.

Example: H 0 : [Tex]\mu = [/Tex] 50 and H 1 : [Tex]\mu \neq 50 [/Tex]

To delve deeper into differences into both types of test: Refer to link

What are Type 1 and Type 2 errors in Hypothesis Testing?

In hypothesis testing, Type I and Type II errors are two possible errors that researchers can make when drawing conclusions about a population based on a sample of data. These errors are associated with the decisions made regarding the null hypothesis and the alternative hypothesis.

• Type I error: When we reject the null hypothesis, although that hypothesis was true. Type I error is denoted by alpha( [Tex]\alpha [/Tex] ).
• Type II errors : When we accept the null hypothesis, but it is false. Type II errors are denoted by beta( [Tex]\beta [/Tex] ).

How does Hypothesis Testing work?

Step 1: define null and alternative hypothesis.

State the null hypothesis ( [Tex]H_0 [/Tex] ), representing no effect, and the alternative hypothesis ( [Tex]H_1 [/Tex] ​), suggesting an effect or difference.

We first identify the problem about which we want to make an assumption keeping in mind that our assumption should be contradictory to one another, assuming Normally distributed data.

Step 2 – Choose significance level

Select a significance level ( [Tex]\alpha [/Tex] ), typically 0.05, to determine the threshold for rejecting the null hypothesis. It provides validity to our hypothesis test, ensuring that we have sufficient data to back up our claims. Usually, we determine our significance level beforehand of the test. The p-value is the criterion used to calculate our significance value.

Step 3 – Collect and Analyze data.

Gather relevant data through observation or experimentation. Analyze the data using appropriate statistical methods to obtain a test statistic.

Step 4-Calculate Test Statistic

The data for the tests are evaluated in this step we look for various scores based on the characteristics of data. The choice of the test statistic depends on the type of hypothesis test being conducted.

There are various hypothesis tests, each appropriate for various goal to calculate our test. This could be a Z-test , Chi-square , T-test , and so on.

• Z-test : If population means and standard deviations are known. Z-statistic is commonly used.
• t-test : If population standard deviations are unknown. and sample size is small than t-test statistic is more appropriate.
• Chi-square test : Chi-square test is used for categorical data or for testing independence in contingency tables
• F-test : F-test is often used in analysis of variance (ANOVA) to compare variances or test the equality of means across multiple groups.

We have a smaller dataset, So, T-test is more appropriate to test our hypothesis.

T-statistic is a measure of the difference between the means of two groups relative to the variability within each group. It is calculated as the difference between the sample means divided by the standard error of the difference. It is also known as the t-value or t-score.

Step 5 – Comparing Test Statistic:

In this stage, we decide where we should accept the null hypothesis or reject the null hypothesis. There are two ways to decide where we should accept or reject the null hypothesis.

Method A: Using Crtical values

Comparing the test statistic and tabulated critical value we have,

• If Test Statistic>Critical Value: Reject the null hypothesis.
• If Test Statistic≤Critical Value: Fail to reject the null hypothesis.

Note: Critical values are predetermined threshold values that are used to make a decision in hypothesis testing. To determine critical values for hypothesis testing, we typically refer to a statistical distribution table , such as the normal distribution or t-distribution tables based on.

Method B: Using P-values

We can also come to an conclusion using the p-value,

• If the p-value is less than or equal to the significance level i.e. ( [Tex]p\leq\alpha [/Tex] ), you reject the null hypothesis. This indicates that the observed results are unlikely to have occurred by chance alone, providing evidence in favor of the alternative hypothesis.
• If the p-value is greater than the significance level i.e. ( [Tex]p\geq \alpha[/Tex] ), you fail to reject the null hypothesis. This suggests that the observed results are consistent with what would be expected under the null hypothesis.

Note : The p-value is the probability of obtaining a test statistic as extreme as, or more extreme than, the one observed in the sample, assuming the null hypothesis is true. To determine p-value for hypothesis testing, we typically refer to a statistical distribution table , such as the normal distribution or t-distribution tables based on.

Step 7- Interpret the Results

At last, we can conclude our experiment using method A or B.

Calculating test statistic

To validate our hypothesis about a population parameter we use statistical functions . We use the z-score, p-value, and level of significance(alpha) to make evidence for our hypothesis for normally distributed data .

1. Z-statistics:

When population means and standard deviations are known.

[Tex]z = \frac{\bar{x} – \mu}{\frac{\sigma}{\sqrt{n}}}[/Tex]

• [Tex]\bar{x} [/Tex] is the sample mean,
• μ represents the population mean, 
• σ is the standard deviation
• and n is the size of the sample.

2. T-Statistics

T test is used when n<30,

t-statistic calculation is given by:

[Tex]t=\frac{x̄-μ}{s/\sqrt{n}} [/Tex]

• t = t-score,
• x̄ = sample mean
• μ = population mean,
• s = standard deviation of the sample,
• n = sample size

3. Chi-Square Test

Chi-Square Test for Independence categorical Data (Non-normally distributed) using:

[Tex]\chi^2 = \sum \frac{(O_{ij} – E_{ij})^2}{E_{ij}}[/Tex]

• [Tex]O_{ij}[/Tex] is the observed frequency in cell [Tex]{ij} [/Tex]
• i,j are the rows and columns index respectively.
• [Tex]E_{ij}[/Tex] is the expected frequency in cell [Tex]{ij}[/Tex] , calculated as : [Tex]\frac{{\text{{Row total}} \times \text{{Column total}}}}{{\text{{Total observations}}}}[/Tex]

Real life Examples of Hypothesis Testing

Let’s examine hypothesis testing using two real life situations,

Case A: D oes a New Drug Affect Blood Pressure?

Imagine a pharmaceutical company has developed a new drug that they believe can effectively lower blood pressure in patients with hypertension. Before bringing the drug to market, they need to conduct a study to assess its impact on blood pressure.

• Before Treatment: 120, 122, 118, 130, 125, 128, 115, 121, 123, 119
• After Treatment: 115, 120, 112, 128, 122, 125, 110, 117, 119, 114

Step 1 : Define the Hypothesis

• Null Hypothesis : (H 0 )The new drug has no effect on blood pressure.
• Alternate Hypothesis : (H 1 )The new drug has an effect on blood pressure.

Step 2: Define the Significance level

Let’s consider the Significance level at 0.05, indicating rejection of the null hypothesis.

If the evidence suggests less than a 5% chance of observing the results due to random variation.

Step 3 : Compute the test statistic

Using paired T-test analyze the data to obtain a test statistic and a p-value.

The test statistic (e.g., T-statistic) is calculated based on the differences between blood pressure measurements before and after treatment.

t = m/(s/√n)

• m  = mean of the difference i.e X after, X before
• s  = standard deviation of the difference (d) i.e d i ​= X after, i ​− X before,
• n  = sample size,

then, m= -3.9, s= 1.8 and n= 10

we, calculate the , T-statistic = -9 based on the formula for paired t test

Step 4: Find the p-value

The calculated t-statistic is -9 and degrees of freedom df = 9, you can find the p-value using statistical software or a t-distribution table.

thus, p-value = 8.538051223166285e-06

Step 5: Result

• If the p-value is less than or equal to 0.05, the researchers reject the null hypothesis.
• If the p-value is greater than 0.05, they fail to reject the null hypothesis.

Conclusion: Since the p-value (8.538051223166285e-06) is less than the significance level (0.05), the researchers reject the null hypothesis. There is statistically significant evidence that the average blood pressure before and after treatment with the new drug is different.

Python Implementation of Case A

Let’s create hypothesis testing with python, where we are testing whether a new drug affects blood pressure. For this example, we will use a paired T-test. We’ll use the scipy.stats library for the T-test.

Scipy is a mathematical library in Python that is mostly used for mathematical equations and computations.

We will implement our first real life problem via python,

import numpy as np from scipy import stats # Data before_treatment = np . array ([ 120 , 122 , 118 , 130 , 125 , 128 , 115 , 121 , 123 , 119 ]) after_treatment = np . array ([ 115 , 120 , 112 , 128 , 122 , 125 , 110 , 117 , 119 , 114 ]) # Step 1: Null and Alternate Hypotheses # Null Hypothesis: The new drug has no effect on blood pressure. # Alternate Hypothesis: The new drug has an effect on blood pressure. null_hypothesis = "The new drug has no effect on blood pressure." alternate_hypothesis = "The new drug has an effect on blood pressure." # Step 2: Significance Level alpha = 0.05 # Step 3: Paired T-test t_statistic , p_value = stats . ttest_rel ( after_treatment , before_treatment ) # Step 4: Calculate T-statistic manually m = np . mean ( after_treatment - before_treatment ) s = np . std ( after_treatment - before_treatment , ddof = 1 ) # using ddof=1 for sample standard deviation n = len ( before_treatment ) t_statistic_manual = m / ( s / np . sqrt ( n )) # Step 5: Decision if p_value <= alpha : decision = "Reject" else : decision = "Fail to reject" # Conclusion if decision == "Reject" : conclusion = "There is statistically significant evidence that the average blood pressure before and after treatment with the new drug is different." else : conclusion = "There is insufficient evidence to claim a significant difference in average blood pressure before and after treatment with the new drug." # Display results print ( "T-statistic (from scipy):" , t_statistic ) print ( "P-value (from scipy):" , p_value ) print ( "T-statistic (calculated manually):" , t_statistic_manual ) print ( f "Decision: { decision } the null hypothesis at alpha= { alpha } ." ) print ( "Conclusion:" , conclusion )

T-statistic (from scipy): -9.0 P-value (from scipy): 8.538051223166285e-06 T-statistic (calculated manually): -9.0 Decision: Reject the null hypothesis at alpha=0.05. Conclusion: There is statistically significant evidence that the average blood pressure before and after treatment with the new drug is different.

In the above example, given the T-statistic of approximately -9 and an extremely small p-value, the results indicate a strong case to reject the null hypothesis at a significance level of 0.05. 

• The results suggest that the new drug, treatment, or intervention has a significant effect on lowering blood pressure.
• The negative T-statistic indicates that the mean blood pressure after treatment is significantly lower than the assumed population mean before treatment.

Case B : Cholesterol level in a population

Data: A sample of 25 individuals is taken, and their cholesterol levels are measured.

Cholesterol Levels (mg/dL): 205, 198, 210, 190, 215, 205, 200, 192, 198, 205, 198, 202, 208, 200, 205, 198, 205, 210, 192, 205, 198, 205, 210, 192, 205.

Populations Mean = 200

Population Standard Deviation (σ): 5 mg/dL(given for this problem)

Step 1: Define the Hypothesis

• Null Hypothesis (H 0 ): The average cholesterol level in a population is 200 mg/dL.
• Alternate Hypothesis (H 1 ): The average cholesterol level in a population is different from 200 mg/dL.

As the direction of deviation is not given , we assume a two-tailed test, and based on a normal distribution table, the critical values for a significance level of 0.05 (two-tailed) can be calculated through the z-table and are approximately -1.96 and 1.96.

The test statistic is calculated by using the z formula Z = [Tex](203.8 – 200) / (5 \div \sqrt{25}) [/Tex] ​ and we get accordingly , Z =2.039999999999992.

Step 4: Result

Since the absolute value of the test statistic (2.04) is greater than the critical value (1.96), we reject the null hypothesis. And conclude that, there is statistically significant evidence that the average cholesterol level in the population is different from 200 mg/dL

Python Implementation of Case B

import scipy.stats as stats import math import numpy as np # Given data sample_data = np . array ( [ 205 , 198 , 210 , 190 , 215 , 205 , 200 , 192 , 198 , 205 , 198 , 202 , 208 , 200 , 205 , 198 , 205 , 210 , 192 , 205 , 198 , 205 , 210 , 192 , 205 ]) population_std_dev = 5 population_mean = 200 sample_size = len ( sample_data ) # Step 1: Define the Hypotheses # Null Hypothesis (H0): The average cholesterol level in a population is 200 mg/dL. # Alternate Hypothesis (H1): The average cholesterol level in a population is different from 200 mg/dL. # Step 2: Define the Significance Level alpha = 0.05 # Two-tailed test # Critical values for a significance level of 0.05 (two-tailed) critical_value_left = stats . norm . ppf ( alpha / 2 ) critical_value_right = - critical_value_left # Step 3: Compute the test statistic sample_mean = sample_data . mean () z_score = ( sample_mean - population_mean ) / \ ( population_std_dev / math . sqrt ( sample_size )) # Step 4: Result # Check if the absolute value of the test statistic is greater than the critical values if abs ( z_score ) > max ( abs ( critical_value_left ), abs ( critical_value_right )): print ( "Reject the null hypothesis." ) print ( "There is statistically significant evidence that the average cholesterol level in the population is different from 200 mg/dL." ) else : print ( "Fail to reject the null hypothesis." ) print ( "There is not enough evidence to conclude that the average cholesterol level in the population is different from 200 mg/dL." )

Reject the null hypothesis. There is statistically significant evidence that the average cholesterol level in the population is different from 200 mg/dL.

Limitations of Hypothesis Testing

• Although a useful technique, hypothesis testing does not offer a comprehensive grasp of the topic being studied. Without fully reflecting the intricacy or whole context of the phenomena, it concentrates on certain hypotheses and statistical significance.
• The accuracy of hypothesis testing results is contingent on the quality of available data and the appropriateness of statistical methods used. Inaccurate data or poorly formulated hypotheses can lead to incorrect conclusions.
• Relying solely on hypothesis testing may cause analysts to overlook significant patterns or relationships in the data that are not captured by the specific hypotheses being tested. This limitation underscores the importance of complimenting hypothesis testing with other analytical approaches.

Hypothesis testing stands as a cornerstone in statistical analysis, enabling data scientists to navigate uncertainties and draw credible inferences from sample data. By systematically defining null and alternative hypotheses, choosing significance levels, and leveraging statistical tests, researchers can assess the validity of their assumptions. The article also elucidates the critical distinction between Type I and Type II errors, providing a comprehensive understanding of the nuanced decision-making process inherent in hypothesis testing. The real-life example of testing a new drug’s effect on blood pressure using a paired T-test showcases the practical application of these principles, underscoring the importance of statistical rigor in data-driven decision-making.

Frequently Asked Questions (FAQs)

1. what are the 3 types of hypothesis test.

There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed. Right-tailed tests assess if a parameter is greater, left-tailed if lesser. Two-tailed tests check for non-directional differences, greater or lesser.

2.What are the 4 components of hypothesis testing?

Null Hypothesis ( [Tex]H_o [/Tex] ): No effect or difference exists. Alternative Hypothesis ( [Tex]H_1 [/Tex] ): An effect or difference exists. Significance Level ( [Tex]\alpha [/Tex] ): Risk of rejecting null hypothesis when it’s true (Type I error). Test Statistic: Numerical value representing observed evidence against null hypothesis.

3.What is hypothesis testing in ML?

Statistical method to evaluate the performance and validity of machine learning models. Tests specific hypotheses about model behavior, like whether features influence predictions or if a model generalizes well to unseen data.

4.What is the difference between Pytest and hypothesis in Python?

Pytest purposes general testing framework for Python code while Hypothesis is a Property-based testing framework for Python, focusing on generating test cases based on specified properties of the code.

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How to Implement Hypothesis-Driven Development

Remember back to the time when we were in high school science class. Our teachers had a framework for helping us learn – an experimental approach based on the best available evidence at hand. We were asked to make observations about the world around us, then attempt to form an explanation or hypothesis to explain what we had observed. We then tested this hypothesis by predicting an outcome based on our theory that would be achieved in a controlled experiment – if the outcome was achieved, we had proven our theory to be correct.

We could then apply this learning to inform and test other hypotheses by constructing more sophisticated experiments, and tuning, evolving or abandoning any hypothesis as we made further observations from the results we achieved.

Experimentation is the foundation of the scientific method, which is a systematic means of exploring the world around us. Although some experiments take place in laboratories, it is possible to perform an experiment anywhere, at any time, even in software development.

Practicing  Hypothesis-Driven Development  is thinking about the development of new ideas, products and services – even organizational change – as a series of experiments to determine whether an expected outcome will be achieved. The process is iterated upon until a desirable outcome is obtained or the idea is determined to be not viable.

We need to change our mindset to view our proposed solution to a problem statement as a hypothesis, especially in new product or service development – the market we are targeting, how a business model will work, how code will execute and even how the customer will use it.

We do not do projects anymore, only experiments. Customer discovery and Lean Startup strategies are designed to test assumptions about customers. Quality Assurance is testing system behavior against defined specifications. The experimental principle also applies in Test-Driven Development – we write the test first, then use the test to validate that our code is correct, and succeed if the code passes the test. Ultimately, product or service development is a process to test a hypothesis about system behaviour in the environment or market it is developed for.

The key outcome of an experimental approach is measurable evidence and learning.

Learning is the information we have gained from conducting the experiment. Did what we expect to occur actually happen? If not, what did and how does that inform what we should do next?

In order to learn we need use the scientific method for investigating phenomena, acquiring new knowledge, and correcting and integrating previous knowledge back into our thinking.

As the software development industry continues to mature, we now have an opportunity to leverage improved capabilities such as Continuous Design and Delivery to maximize our potential to learn quickly what works and what does not. By taking an experimental approach to information discovery, we can more rapidly test our solutions against the problems we have identified in the products or services we are attempting to build. With the goal to optimize our effectiveness of solving the right problems, over simply becoming a feature factory by continually building solutions.

The steps of the scientific method are to:

• Make observations
• Formulate a hypothesis
• Design an experiment to test the hypothesis
• State the indicators to evaluate if the experiment has succeeded
• Conduct the experiment
• Evaluate the results of the experiment
• Accept or reject the hypothesis
• If necessary, make and test a new hypothesis

Using an experimentation approach to software development

We need to challenge the concept of having fixed requirements for a product or service. Requirements are valuable when teams execute a well known or understood phase of an initiative, and can leverage well understood practices to achieve the outcome. However, when you are in an exploratory, complex and uncertain phase you need hypotheses.

Handing teams a set of business requirements reinforces an order-taking approach and mindset that is flawed.

Business does the thinking and ‘knows’ what is right. The purpose of the development team is to implement what they are told. But when operating in an area of uncertainty and complexity, all the members of the development team should be encouraged to think and share insights on the problem and potential solutions. A team simply taking orders from a business owner is not utilizing the full potential, experience and competency that a cross-functional multi-disciplined team offers.

Framing hypotheses

The traditional user story framework is focused on capturing requirements for what we want to build and for whom, to enable the user to receive a specific benefit from the system.

As A…. <role>

I Want… <goal/desire>

So That… <receive benefit>

Behaviour Driven Development (BDD) and Feature Injection  aims to improve the original framework by supporting communication and collaboration between developers, tester and non-technical participants in a software project.

In Order To… <receive benefit>

As A… <role>

When viewing work as an experiment, the traditional story framework is insufficient. As in our high school science experiment, we need to define the steps we will take to achieve the desired outcome. We then need to state the specific indicators (or signals) we expect to observe that provide evidence that our hypothesis is valid. These need to be stated before conducting the test to reduce biased interpretations of the results. 

If we observe signals that indicate our hypothesis is correct, we can be more confident that we are on the right path and can alter the user story framework to reflect this.

Therefore, a user story structure to support Hypothesis-Driven Development would be;

We believe < this capability >

What functionality we will develop to test our hypothesis? By defining a ‘test’ capability of the product or service that we are attempting to build, we identify the functionality and hypothesis we want to test.

Will result in < this outcome >

What is the expected outcome of our experiment? What is the specific result we expect to achieve by building the ‘test’ capability?

We will know we have succeeded when < we see a measurable signal >

What signals will indicate that the capability we have built is effective? What key metrics (qualitative or quantitative) we will measure to provide evidence that our experiment has succeeded and give us enough confidence to move to the next stage.

The threshold you use for statistically significance will depend on your understanding of the business and context you are operating within. Not every company has the user sample size of Amazon or Google to run statistically significant experiments in a short period of time. Limits and controls need to be defined by your organization to determine acceptable evidence thresholds that will allow the team to advance to the next step.

For example if you are building a rocket ship you may want your experiments to have a high threshold for statistical significance. If you are deciding between two different flows intended to help increase user sign up you may be happy to tolerate a lower significance threshold.

The final step is to clearly and visibly state any assumptions made about our hypothesis, to create a feedback loop for the team to provide further input, debate and understanding of the circumstance under which we are performing the test. Are they valid and make sense from a technical and business perspective?

Hypotheses when aligned to your MVP can provide a testing mechanism for your product or service vision. They can test the most uncertain areas of your product or service, in order to gain information and improve confidence.

Examples of Hypothesis-Driven Development user stories are;

Business story

We Believe That increasing the size of hotel images on the booking page

Will Result In improved customer engagement and conversion

We Will Know We Have Succeeded When we see a 5% increase in customers who review hotel images who then proceed to book in 48 hours.

It is imperative to have effective monitoring and evaluation tools in place when using an experimental approach to software development in order to measure the impact of our efforts and provide a feedback loop to the team. Otherwise we are essentially blind to the outcomes of our efforts.

In agile software development we define working software as the primary measure of progress.

By combining Continuous Delivery and Hypothesis-Driven Development we can now define working software and validated learning as the primary measures of progress.

Ideally we should not say we are done until we have measured the value of what is being delivered – in other words, gathered data to validate our hypothesis.

Examples of how to gather data is performing A/B Testing to test a hypothesis and measure to change in customer behaviour. Alternative testings options can be customer surveys, paper prototypes, user and/or guerrilla testing.

One example of a company we have worked with that uses Hypothesis-Driven Development is  lastminute.com . The team formulated a hypothesis that customers are only willing to pay a max price for a hotel based on the time of day they book. Tom Klein, CEO and President of Sabre Holdings shared  the story  of how they improved conversion by 400% within a week.

Combining practices such as Hypothesis-Driven Development and Continuous Delivery accelerates experimentation and amplifies validated learning. This gives us the opportunity to accelerate the rate at which we innovate while relentlessly reducing cost, leaving our competitors in the dust. Ideally we can achieve the ideal of one piece flow: atomic changes that enable us to identify causal relationships between the changes we make to our products and services, and their impact on key metrics.

As Kent Beck said, “Test-Driven Development is a great excuse to think about the problem before you think about the solution”. Hypothesis-Driven Development is a great opportunity to test what you think the problem is, before you work on the solution.

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How to Implement Hypothesis-Driven Development

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Remember back to the time when we were in high school science class. Our teachers had a framework for helping us learn – an experimental approach based on the best available evidence at hand. We were asked to make observations about the world around us, then attempt to form an explanation or hypothesis to explain what we had observed. We then tested this hypothesis by predicting an outcome based on our theory that would be achieved in a controlled experiment – if the outcome was achieved, we had proven our theory to be correct.

We could then apply this learning to inform and test other hypotheses by constructing more sophisticated experiments, and tuning, evolving, or abandoning any hypothesis as we made further observations from the results we achieved.

Experimentation is the foundation of the scientific method, which is a systematic means of exploring the world around us. Although some experiments take place in laboratories, it is possible to perform an experiment anywhere, at any time, even in software development.

Practicing Hypothesis-Driven Development [1] is thinking about the development of new ideas, products, and services – even organizational change – as a series of experiments to determine whether an expected outcome will be achieved. The process is iterated upon until a desirable outcome is obtained or the idea is determined to be not viable.

We need to change our mindset to view our proposed solution to a problem statement as a hypothesis, especially in new product or service development – the market we are targeting, how a business model will work, how code will execute and even how the customer will use it.

We do not do projects anymore, only experiments. Customer discovery and Lean Startup strategies are designed to test assumptions about customers. Quality Assurance is testing system behavior against defined specifications. The experimental principle also applies in Test-Driven Development – we write the test first, then use the test to validate that our code is correct, and succeed if the code passes the test. Ultimately, product or service development is a process to test a hypothesis about system behavior in the environment or market it is developed for.

The key outcome of an experimental approach is measurable evidence and learning. Learning is the information we have gained from conducting the experiment. Did what we expect to occur actually happen? If not, what did and how does that inform what we should do next?

In order to learn we need to use the scientific method for investigating phenomena, acquiring new knowledge, and correcting and integrating previous knowledge back into our thinking.

As the software development industry continues to mature, we now have an opportunity to leverage improved capabilities such as Continuous Design and Delivery to maximize our potential to learn quickly what works and what does not. By taking an experimental approach to information discovery, we can more rapidly test our solutions against the problems we have identified in the products or services we are attempting to build. With the goal to optimize our effectiveness of solving the right problems, over simply becoming a feature factory by continually building solutions.

The steps of the scientific method are to:

• Make observations
• Formulate a hypothesis
• Design an experiment to test the hypothesis
• State the indicators to evaluate if the experiment has succeeded
• Conduct the experiment
• Evaluate the results of the experiment
• Accept or reject the hypothesis
• If necessary, make and test a new hypothesis

Using an experimentation approach to software development

We need to challenge the concept of having fixed requirements for a product or service. Requirements are valuable when teams execute a well known or understood phase of an initiative and can leverage well-understood practices to achieve the outcome. However, when you are in an exploratory, complex and uncertain phase you need hypotheses. Handing teams a set of business requirements reinforces an order-taking approach and mindset that is flawed. Business does the thinking and ‘knows’ what is right. The purpose of the development team is to implement what they are told. But when operating in an area of uncertainty and complexity, all the members of the development team should be encouraged to think and share insights on the problem and potential solutions. A team simply taking orders from a business owner is not utilizing the full potential, experience and competency that a cross-functional multi-disciplined team offers.

Framing Hypotheses

The traditional user story framework is focused on capturing requirements for what we want to build and for whom, to enable the user to receive a specific benefit from the system.

As A…. <role>

I Want… <goal/desire>

So That… <receive benefit>

Behaviour Driven Development (BDD) and Feature Injection aims to improve the original framework by supporting communication and collaboration between developers, tester and non-technical participants in a software project.

In Order To… <receive benefit>

As A… <role>

When viewing work as an experiment, the traditional story framework is insufficient. As in our high school science experiment, we need to define the steps we will take to achieve the desired outcome. We then need to state the specific indicators (or signals) we expect to observe that provide evidence that our hypothesis is valid. These need to be stated before conducting the test to reduce the bias of interpretation of results.

If we observe signals that indicate our hypothesis is correct, we can be more confident that we are on the right path and can alter the user story framework to reflect this.

Therefore, a user story structure to support Hypothesis-Driven Development would be;

We believe < this capability >

What functionality we will develop to test our hypothesis? By defining a ‘test’ capability of the product or service that we are attempting to build, we identify the functionality and hypothesis we want to test.

Will result in < this outcome >

What is the expected outcome of our experiment? What is the specific result we expect to achieve by building the ‘test’ capability?

We will have confidence to proceed when < we see a measurable signal >

What signals will indicate that the capability we have built is effective? What key metrics (qualitative or quantitative) we will measure to provide evidence that our experiment has succeeded and give us enough confidence to move to the next stage.

The threshold you use for statistical significance will depend on your understanding of the business and context you are operating within. Not every company has the user sample size of Amazon or Google to run statistically significant experiments in a short period of time. Limits and controls need to be defined by your organization to determine acceptable evidence thresholds that will allow the team to advance to the next step.

For example, if you are building a rocket ship you may want your experiments to have a high threshold for statistical significance. If you are deciding between two different flows intended to help increase user sign up you may be happy to tolerate a lower significance threshold.

The final step is to clearly and visibly state any assumptions made about our hypothesis, to create a feedback loop for the team to provide further input, debate, and understanding of the circumstance under which we are performing the test. Are they valid and make sense from a technical and business perspective?

Hypotheses, when aligned to your MVP, can provide a testing mechanism for your product or service vision. They can test the most uncertain areas of your product or service, in order to gain information and improve confidence.

Examples of Hypothesis-Driven Development user stories are;

Business story.

We Believe That increasing the size of hotel images on the booking page Will Result In improved customer engagement and conversion We Will Have Confidence To Proceed When  we see a 5% increase in customers who review hotel images who then proceed to book in 48 hours.

It is imperative to have effective monitoring and evaluation tools in place when using an experimental approach to software development in order to measure the impact of our efforts and provide a feedback loop to the team. Otherwise, we are essentially blind to the outcomes of our efforts.

In agile software development, we define working software as the primary measure of progress. By combining Continuous Delivery and Hypothesis-Driven Development we can now define working software and validated learning as the primary measures of progress.

Ideally, we should not say we are done until we have measured the value of what is being delivered – in other words, gathered data to validate our hypothesis.

Examples of how to gather data is performing A/B Testing to test a hypothesis and measure to change in customer behavior. Alternative testings options can be customer surveys, paper prototypes, user and/or guerilla testing.

One example of a company we have worked with that uses Hypothesis-Driven Development is lastminute.com . The team formulated a hypothesis that customers are only willing to pay a max price for a hotel based on the time of day they book. Tom Klein, CEO and President of Sabre Holdings shared the story  of how they improved conversion by 400% within a week.

Combining practices such as Hypothesis-Driven Development and Continuous Delivery accelerates experimentation and amplifies validated learning. This gives us the opportunity to accelerate the rate at which we innovate while relentlessly reducing costs, leaving our competitors in the dust. Ideally, we can achieve the ideal of one-piece flow: atomic changes that enable us to identify causal relationships between the changes we make to our products and services, and their impact on key metrics.

As Kent Beck said, “Test-Driven Development is a great excuse to think about the problem before you think about the solution”. Hypothesis-Driven Development is a great opportunity to test what you think the problem is before you work on the solution.

We also run a  workshop to help teams implement Hypothesis-Driven Development . Get in touch to run it at your company. 

[1]  Hypothesis-Driven Development  By Jeffrey L. Taylor

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6 Steps Of Hypothesis-Driven Development That Works

One of the greatest fears of product managers is to create an app that flopped because it's based on untested assumptions. After successfully launching more than 20 products, we're convinced that we've found the right approach for hypothesis-driven development.

In this guide, I'll show you how we validated the hypotheses to ensure that the apps met the users' expectations and needs.

What is hypothesis-driven development?

Hypothesis-driven development is a prototype methodology that allows product designers to develop, test, and rebuild a product until it’s acceptable by the users. It is an iterative measure that explores assumptions defined during the project and attempts to validate it with users’ feedbacks.

What you have assumed during the initial stage of development may not be valid for the users. Even if they are backed by historical data, user behaviors can be affected by specific audiences and other factors. Hypothesis-driven development removes these uncertainties as the project progresses. 

Why we use hypothesis-driven development

For us, the hypothesis-driven approach provides a structured way to consolidate ideas and build hypotheses based on objective criteria. It’s also less costly to test the prototype before production.

Using this approach has reliably allowed us to identify what, how, and in which order should the testing be done. It gives us a deep understanding of how we prioritise the features, how it’s connected to the business goals and desired user outcomes.

We’re also able to track and compare the desired and real outcomes of developing the features. 

The process of Prototype Development that we use

Our success in building apps that are well-accepted by users is based on the Lean UX definition of hypothesis. We believe that the business outcome will be achieved if the user’s outcome is fulfilled for the particular feature. 

Here’s the process flow:

How Might We technique → Dot voting (based on estimated/assumptive impact) → converting into a hypothesis → define testing methodology (research method + success/fail criteria) → impact effort scale for prioritizing → test, learn, repeat.

Once the hypothesis is proven right, the feature is escalated into the development track for UI design and development. 

Step 1: List Down Questions And Assumptions

Whether it’s the initial stage of the project or after the launch, there are always uncertainties or ideas to further improve the existing product. In order to move forward, you’ll need to turn the ideas into structured hypotheses where they can be tested prior to production.  

To start with, jot the ideas or assumptions down on paper or a sticky note. 

Then, you’ll want to widen the scope of the questions and assumptions into possible solutions. The How Might We (HMW) technique is handy in rephrasing the statements into questions that facilitate brainstorming.

For example, if you have a social media app with a low number of users, asking, “How might we increase the number of users for the app?” makes brainstorming easier. 

Step 2: Dot Vote to Prioritize Questions and Assumptions

Once you’ve got a list of questions, it’s time to decide which are potentially more impactful for the product. The Dot Vote method, where team members are given dots to place on the questions, helps prioritize the questions and assumptions. 

Our team uses this method when we’re faced with many ideas and need to eliminate some of them. We started by grouping similar ideas and use 3-5 dots to vote. At the end of the process, we’ll have the preliminary data on the possible impact and our team’s interest in developing certain features. 

This method allows us to prioritize the statements derived from the HMW technique and we’re only converting the top ones. 

Step 3: Develop Hypotheses from Questions

The questions lead to a brainstorming session where the answers become hypotheses for the product. The hypothesis is meant to create a framework that allows the questions and solutions to be defined clearly for validation.

Our team followed a specific format in forming hypotheses. We structured the statement as follow:

We believe we will achieve [ business outcome], 

If [ the persona],

Solve their need in  [ user outcome] using [feature]. ‍

Here’s a hypothesis we’ve created:

We believe we will achieve DAU=100 if Mike (our proto persona) solve their need in recording and sharing videos instantaneously using our camera and cloud storage .

Step 4: Test the Hypothesis with an Experiment

It’s crucial to validate each of the assumptions made on the product features. Based on the hypotheses, experiments in the form of interviews, surveys, usability testing, and so forth are created to determine if the assumptions are aligned with reality. 

Each of the methods provides some level of confidence. Therefore, you don’t want to be 100% reliant on a particular method as it’s based on a sample of users.

It’s important to choose a research method that allows validation to be done with minimal effort. Even though hypotheses validation provides a degree of confidence, not all assumptions can be tested and there could be a margin of error in data obtained as the test is conducted on a sample of people. 

The experiments are designed in such a way that feedback can be compared with the predicted outcome. Only validated hypotheses are brought forward for development.

Testing all the hypotheses can be tedious. To be more efficient, you can use the impact effort scale. This method allows you to focus on hypotheses that are potentially high value and easy to validate. 

You can also work on hypotheses that deliver high impact but require high effort. Ignore those that require high impact but low impact and keep hypotheses with low impact and effort into the backlog. 

At Uptech, we assign each hypothesis with clear testing criteria. We rank the hypothesis with a binary ‘task success’ and subjective ‘effort on task’ where the latter is scored from 1 to 10. 

While we’re conducting the test, we also collect qualitative data such as the users' feedback. We have a habit of segregation the feedback into pros, cons and neutral with color-coded stickers.  (red - cons, green -pros, blue- neutral).

The best practice is to test each hypothesis at least on 5 users. 

Step 5  Learn, Build (and Repeat)

The hypothesis-driven approach is not a single-ended process. Often, you’ll find that some of the hypotheses are proven to be false. Rather than be disheartened, you should use the data gathered to finetune the hypothesis and design a better experiment in the next phase.

Treat the entire cycle as a learning process where you’ll better understand the product and the customers. 

We’ve found the process helpful when developing an MVP for Carbon Club, an environmental startup in the UK. The app allows users to donate to charity based on the carbon-footprint produced. 

In order to calculate the carbon footprint, we’re weighing the options of

• Connecting the app to the users’ bank account to monitor the carbon footprint based on purchases made.
• Allowing users to take quizzes on their lifestyles.

Upon validation, we’ve found that all of the users opted for the second option as they are concerned about linking an unknown app to their banking account. 

The result makes us shelves the first assumption we’ve made during pre-Sprint research. It also saves our client $50,000, and a few months of work as connecting the app to the bank account requires a huge effort. 

Step 6: Implement Product and Maintain

Once you’ve got the confidence that the remaining hypotheses are validated, it’s time to develop the product. However, testing must be continued even after the product is launched. 

You should be on your toes as customers’ demands, market trends, local economics, and other conditions may require some features to evolve. 

Our takeaways for hypothesis-driven development

If there’s anything that you could pick from our experience, it’s these 5 points.

1. Should every idea go straight into the backlog? No, unless they are validated with substantial evidence. 

2. While it’s hard to define business outcomes with specific metrics and desired values, you should do it anyway. Try to be as specific as possible, and avoid general terms. Give your best effort and adjust as you receive new data.  

3. Get all product teams involved as the best ideas are born from collaboration.

4. Start with a plan consists of 2 main parameters, i.e., criteria of success and research methods. Besides qualitative insights, you need to set objective criteria to determine if a test is successful. Use the Test Card to validate the assumptions strategically. 

5. The methodology that we’ve recommended in this article works not only for products. We’ve applied it at the end of 2019 for setting the strategic goals of the company and end up with robust results, engaged and aligned team.

You'll have a better idea of which features would lead to a successful product with hypothesis-driven development. Rather than vague assumptions, the consolidated data from users will provide a clear direction for your development team. 

As for the hypotheses that don't make the cut, improvise, re-test, and leverage for future upgrades.

Keep failing with product launches? I'll be happy to point you in the right direction. Drop me a message here.

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Understanding Hypothesis-Driven Development in Software Development

• February 9, 2024

In software development, there are various approaches and methodologies that developers employ to ensure the successful delivery of high-quality products. One such approach that has gained significant traction in recent years is Hypothesis-Driven Development (HDD). HDD is a mindset that drives the development process by formulating and validating hypotheses to guide decision-making at each stage of development.

The Concept of Hypothesis-Driven Development

Hypothesis-Driven Development (HDD) is a systematic and iterative approach that leverages the scientific method to inform software development decisions. It is based on the premise that by formulating hypotheses and conducting experiments, developers can gather empirical evidence and make informed choices during the development process . The essence of HDD lies in embracing uncertainty and treating software development as a learning process .

Defining Hypothesis-Driven Development

At its core, HDD involves framing hypotheses about user behavior , product features, or system performance and then designing experiments to test these hypotheses. These experiments can take the form of A/B tests, user feedback sessions, or performance benchmarks. For example, let’s say a development team wants to improve the user interface of their application. They might hypothesize that by simplifying the navigation menu, users will find it easier to navigate through the app. To test this hypothesis, they could conduct A/B tests where one group of users sees the original menu and another group sees the simplified menu. By analyzing the data collected from these experiments, the team can make data-driven decisions and improve the overall quality of the product.

Furthermore, HDD encourages developers to iterate and refine their hypotheses based on the experiment results. This iterative process allows for continuous learning and improvement . For instance, if the A/B test results show that the simplified navigation menu did not lead to a significant improvement in user experience, the team can go back to the drawing board and formulate new hypotheses to test. This flexibility and adaptability are key aspects of HDD that enable developers to respond to changing user needs and market demands.

The Importance of Hypothesis-Driven Development

The significance of HDD lies in its ability to mitigate assumptions and biases that can often creep into the development process. Assumptions and biases can lead to misguided decisions and wasted resources. By relying on empirical evidence, developers can make better-informed decisions and ensure that their efforts are aligned with user needs and expectations. HDD provides a structured framework for gathering and analyzing data, allowing developers to make evidence-based choices rather than relying solely on intuition or personal opinions.

Moreover, HDD fosters a culture of continuous improvement within development teams. It encourages teams to learn from their failures and adapt their hypotheses accordingly. Instead of viewing failures as setbacks, HDD treats them as valuable learning opportunities. By embracing failure as a stepping stone to success, teams can identify areas for improvement and make adjustments to their hypotheses and experiments. This iterative process not only enhances the quality of the software being developed but also promotes a growth mindset among team members.

In conclusion, Hypothesis-Driven Development is a powerful approach that brings the scientific method into software development. By formulating hypotheses, conducting experiments, and analyzing data , developers can make informed decisions, mitigate assumptions and biases, and foster a culture of continuous improvement. Embracing HDD allows teams to create software that is truly aligned with user needs and expectations, ultimately leading to a better user experience and increased success in the market.

The Process of Hypothesis-Driven Development

The HDD process can be broken down into several key steps that provide a structured framework for developers to follow:

Identifying the Hypothesis

The first step in HDD involves formulating clear and testable hypotheses that address specific areas of uncertainty or improvement in the product. These hypotheses can range from user experience enhancements to performance optimizations. For example, a hypothesis could be that improving the loading time of a website will lead to a decrease in bounce rate and an increase in user engagement. The key is to ensure that the hypotheses are measurable and actionable.

Developers may gather insights from user feedback, market research, or data analysis to identify areas that need improvement. By understanding the pain points and challenges faced by users, developers can formulate hypotheses that directly address these issues.

Designing the Experiment

Once the hypotheses are defined, the next step is to design experiments that will validate or invalidate the hypotheses. These experiments should be well-planned and controlled to ensure accurate results. Collaborating with cross-functional teams, such as designers and product managers, can help in designing comprehensive experiments.

For instance, in the example of improving website loading time, the experiment could involve creating two versions of the website – one with the optimized loading time and another with the current loading time. Randomly assigning users to each version and measuring metrics such as bounce rate, page views, and conversion rate can help determine the impact of the loading time improvement.

Implementing the Experiment

After the experiment design is finalized, the actual implementation takes place. This may involve making changes to the software, setting up the necessary data tracking systems, or conducting user tests. It is essential to meticulously follow the experimental design to ensure accurate data collection.

In the case of improving website loading time, developers may need to optimize code, compress images, or leverage caching techniques to achieve the desired improvement. They may also need to set up analytics tools to track user behavior and gather relevant data for analysis.

Analyzing the Results

Once the experiment has been executed, the data collected needs to be analyzed. This analysis aims to draw conclusions about the validity of the hypotheses and the impact of the changes made. It is important to use statistical methods to ensure the reliability of the results.

For example, statistical tests such as t-tests or chi-square tests can be used to determine if the observed differences in metrics between the two versions of the website are statistically significant. This analysis helps developers make informed decisions about the effectiveness of their hypotheses and the potential impact on the product.

By following this iterative process, developers can constantly refine their hypotheses and adjust their development strategies based on the emerging insights. This ensures that the final product aligns with user expectations and provides a seamless experience. Continuous experimentation and data-driven decision-making are at the core of hypothesis-driven development, enabling developers to create products that truly meet the needs of their users.

Benefits of Hypothesis-Driven Development

HDD offers numerous benefits that contribute to the success of software development projects:

Enhancing Product Quality

By adopting a hypothesis-driven approach, developers can make evidence-based decisions that improve the overall quality of the product. Through continuous experimentation and feedback, the product can be refined to meet the specific needs and desires of the users.

For example, let’s say a development team is working on a mobile app that offers a personalized shopping experience. By using HDD, they can hypothesize that adding a recommendation engine based on user preferences will enhance the product’s quality. They can then test this hypothesis by implementing the feature and gathering feedback from a group of users. If the results show that the recommendation engine indeed improves the user experience and leads to more purchases, the team can confidently integrate it into the final product.

Reducing Development Time

HDD helps in reducing development time by enabling developers to focus their efforts on features and enhancements that are proven to provide value to users. By avoiding unnecessary guesswork and speculation, developers can streamline the development process and deliver products faster.

Consider a scenario where a software development team is tasked with creating a project management tool. Instead of spending months building all possible features, they can use HDD to prioritize the most critical functionalities based on user needs. They can formulate hypotheses about which features will have the most significant impact on productivity and test them through iterative development cycles. This approach allows the team to release a minimum viable product quickly and gather real-world feedback, which can then inform further development and reduce time wasted on unnecessary features.

Improving Team Collaboration

Collaboration is a key aspect of HDD, as it involves cross-functional teams working together to design experiments, analyze results, and make informed decisions. This collaboration fosters a sense of shared ownership and drives innovation, as different perspectives and expertise contribute to the development process.

Imagine a development team where designers, developers, and product managers work in silos, rarely communicating or sharing ideas. By implementing HDD, these teams can come together to formulate hypotheses and design experiments that address user pain points. Through collaborative analysis of results, they can gain a deeper understanding of user needs and preferences, leading to more innovative solutions. This shared ownership and collaborative spirit not only improves the development process but also creates a positive work environment where everyone feels valued and empowered.

Challenges in Hypothesis-Driven Development

While Hypothesis-Driven Development (HDD) offers significant advantages, it is not without its challenges. Awareness of these challenges can help development teams proactively tackle them, ensuring a smoother implementation and maximizing the benefits of HDD.

Potential Risks and How to Mitigate Them

Implementing HDD requires careful consideration of potential risks to ensure accurate and reliable results. One of the key risks is data integrity issues, which can arise from incomplete or inaccurate data collection. To mitigate this risk, development teams should establish robust data collection methodologies, including clear guidelines for data entry and validation processes. Regular data audits and quality checks can help maintain the integrity of the collected data.

Another risk associated with HDD is variable user behavior. Users may exhibit different preferences, habits, or responses to the changes introduced through HDD. To address this, development teams should conduct controlled experiments, carefully selecting a diverse range of users to participate in testing. By including users with different backgrounds, demographics, and usage patterns, teams can gather a more comprehensive understanding of how their product or feature performs across various user segments.

Lastly, premature conclusions can pose a risk to the effectiveness of HDD. It is crucial to avoid drawing hasty conclusions based on limited data or early results. Instead, development teams should adopt a data-driven approach, collecting sufficient data and conducting thorough analysis before making any final judgments. This may involve setting clear success metrics and monitoring them over an extended period to ensure accurate evaluation.

Overcoming Resistance to Change

Adopting HDD may face resistance from team members who are more accustomed to traditional development approaches. It is natural for individuals to be hesitant about embracing new methodologies, especially if they have been successful with their existing practices. To overcome this resistance, effective communication and education are essential.

Development teams should clearly articulate the benefits of HDD, emphasizing how it can lead to faster iterations, improved product quality, and increased customer satisfaction. By highlighting the positive outcomes that HDD can bring, team members are more likely to understand and appreciate the value of this approach.

In addition to communication, providing support and training to team members during the transition can also help alleviate resistance. Offering workshops, seminars, or one-on-one coaching sessions can equip team members with the necessary skills and knowledge to effectively apply HDD in their work. This proactive approach ensures that everyone is on the same page and empowers team members to embrace the change with confidence.

Future of Hypothesis-Driven Development

The future of Hypothesis-Driven Development (HDD) looks promising, as it aligns well with the growing emphasis on agile practices and data-driven decision-making in the software development community. However, the potential of HDD goes beyond its current state, with several trends shaping its future and the role it plays in agile practices.

Trends Shaping Hypothesis-Driven Development

One of the notable trends shaping HDD is the increasing availability of data collection and analysis tools. These tools streamline the experimentation process and provide actionable insights, making it easier for development teams to adopt HDD principles. With the advancements in data analytics, teams can now gather and analyze vast amounts of data, enabling them to make more informed decisions based on real-time user feedback.

Furthermore, the integration of machine learning and artificial intelligence techniques presents exciting possibilities for further enhancing HDD methodologies. By leveraging these technologies, development teams can automate the hypothesis generation process, allowing for faster iterations and more accurate predictions. This integration also enables the identification of patterns and correlations in data that might be missed by human analysts, leading to more robust and reliable hypotheses.

The Role of Hypothesis-Driven Development in Agile Practices

HDD complements agile practices by providing a structured and iterative approach to product development. It aligns well with the agile principle of delivering working software incrementally and responding to change. By incorporating HDD into their agile workflows, development teams can effectively balance the need for rapid iterations with the importance of data-driven decision-making.

Moreover, HDD promotes collaboration and cross-functional teamwork within development teams. By encouraging the formulation and testing of hypotheses, HDD fosters a culture of shared learning and continuous improvement. It encourages team members to challenge assumptions, share insights, and work together towards a common goal of delivering high-quality software that meets user needs and expectations.

In conclusion, understanding and embracing Hypothesis-Driven Development is crucial for software development teams seeking to deliver high-quality products that meet user needs and expectations. By adopting a scientific approach and constantly testing and refining hypotheses, developers can make informed decisions, reduce development time, and enhance collaboration. As the future of software development continues to evolve, HDD will play a vital role in ensuring the success of development projects. The trends shaping HDD, such as the increasing availability of data collection and analysis tools and the integration of machine learning and artificial intelligence, will further enhance its effectiveness and enable teams to deliver even more innovative and user-centric solutions.

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What is hypothesis-driven development?

Uncertainty is one of the biggest challenges of modern product development. Most often, there are more question marks than answers available.

This fact forces us to work in an environment of ambiguity and unpredictability.

Instead of combatting this, we should embrace the circumstances and use tools and solutions that excel in ambiguity. One of these tools is a hypothesis-driven approach to development.

Hypothesis-driven development in a nutshell

As the name suggests, hypothesis-driven development is an approach that focuses development efforts around, you guessed it, hypotheses.

To make this example more tangible, let’s compare it to two other common development approaches: feature-driven and outcome-driven.

In feature-driven development, we prioritize our work and effort based on specific features we planned and decided on upfront. The underlying goal here is predictability.

In outcome-driven development, the priorities are dictated not by specific features but by broader outcomes we want to achieve. This approach helps us maximize the value generated.

When it comes to hypothesis-driven development, the development effort is focused first and foremost on validating the most pressing hypotheses the team has. The goal is to maximize learning speed over all else.

Benefits of hypothesis-driven development

There are numerous benefits of a hypothesis-driven approach to development, but the main ones include:

Continuous learning

Mvp mindset, data-driven decision-making.

Hypothesis-driven development maximizes the amount of knowledge the team acquires with each release.

After all, if all you do is test hypotheses, each test must bring you some insight:

Hypothesis-driven development centers the whole prioritization and development process around learning.

Instead of designing specific features or focusing on big, multi-release outcomes, a hypothesis-driven approach forces you to focus on minimum viable solutions ( MVPs ).

After all, the primary thing you are aiming for is hypothesis validation. It often doesn’t require scalability, perfect user experience, and fully-fledged features.

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By definition, hypothesis-driven development forces you to truly focus on MVPs and avoid overcomplicating.

In hypothesis-driven development, each release focuses on testing a particular assumption. That test then brings you new data points, which help you formulate and prioritize next hypotheses.

That’s truly a data-driven development loop that leaves little room for HiPPOs (the highest-paid person in the room’s opinion).

Guide to hypothesis-driven development

Let’s take a look at what hypothesis-driven development looks like in practice. On a high level, it consists of four steps:

• Formulate a list of hypotheses and assumptions
• Prioritize the list
• Design an MVP
• Test and repeat

1. Formulate hypotheses

The first step is to list all hypotheses you are interested in.

Everything you wish to know about your users and market, as well as things you believe you know but don’t have tangible evidence to support, is a form of a hypothesis.

At this stage, I’m not a big fan of robust hypotheses such as, “We believe that if <we do something> then <something will happen> because <some user action>.”

To have such robust hypotheses, you need a solid enough understanding of your users, and if you do have it, then odds are you don’t need hypothesis-driven development anymore.

Instead, I prefer simpler statements that are closer to assumptions than hypotheses, such as:

• “Our users will love the feature X”
• “The option to do X is very important for student segment”
• “Exam preparation is an important and underserved need that our users have”

2. Prioritize

The next step in hypothesis-driven development is to prioritize all assumptions and hypotheses you have. This will create your product backlog:

There are various prioritization frameworks and approaches out there, so choose whichever you prefer. I personally prioritize assumptions based on two main criteria:

• How much will we gain if we positively validate the hypothesis?
• How much will we learn during the validation process?

Your priorities, however, might differ depending on your current context.

3. Design an MVP

Hypothesis-driven development is centered around the idea of MVPs — that is, the smallest possible releases that will help you gather enough information to validate whether a given hypothesis is true.

User experience, maintainability, and product excellence are secondary.

4. Test and repeat

The last step is to launch the MVP and validate whether the actual impact and consequent user behavior validate or invalidate the initial hypothesis.

The success isn’t measured by whether the hypothesis turned out to be accurate, but by how many new insights and learnings you captured during the process.

Based on the experiment, revisit your current list of assumptions, and, if needed, adjust the priority list.

Challenges of hypothesis-driven development

Although hypothesis-driven development comes with great benefits, it’s not all wine and roses.

Let’s take a look at a few core challenges that come with a hypothesis-focused approach.

Lack of robust product experience

Focusing on validating hypotheses and underlying MVP mindset comes at a cost. Robust product experience and great UX often require polishes, optimizations, and iterations, which go against speed-focused hypothesis-driven development.

You can’t optimize for both learning and quality simultaneously.

Unfocused direction

Although hypothesis-driven development is great for gathering initial learnings, eventually, you need to start developing a focused and sustainable long-term product strategy. That’s where outcome-driven development shines.

There’s an infinite amount of explorations you can do, but at some point, you must flip the switch and narrow down your focus around particular outcomes.

Over-emphasis on MVPs

Teams that embrace a hypothesis-driven approach often fall into the trap of an “MVP only” approach. However, shipping an actual prototype is not the only way to validate an assumption or hypothesis.

You can utilize tools such as user interviews, usability tests, market research, or willingness to pay (WTP) experiments to validate most of your doubts.

There’s a thin line between being MVP-focused in development and overusing MVPs as a validation tool.

When to use hypothesis-driven development

As you’ve most likely noticed, a hypothesis-driven development isn’t a multi-tool solution that can be used in every context.

On the contrary, its challenges make it an unsuitable development strategy for many companies.

As a rule of thumb, hypothesis-driven development works best in early-stage products with a high dose of ambiguity. Focusing on hypotheses helps bring enough clarity for the product team to understand where even to focus:

But once you discover your product-market fit and have a solid idea for your long-term strategy, it’s often better to shift into more outcome-focused development. You should still optimize for learning, but it should no longer be the primary focus of your development effort.

While at it, you might also consider feature-driven development as a next step. However, that works only under particular circumstances where predictability is more important than the impact itself — for example, B2B companies delivering custom solutions for their clients or products focused on compliance.

Hypothesis-driven development can be a powerful learning-maximization tool. Its focus on MVP, continuous learning process, and inherent data-driven approach to decision-making are great tools for reducing uncertainty and discovering a path forward in ambiguous settings.

Honestly, the whole process doesn’t differ much from other development processes. The primary difference is that backlog and priories focus on hypotheses rather than features or outcomes.

Start by listing your assumptions, prioritizing them as you would any other backlog, and working your way top-to-bottom by shipping MVPs and adjusting priorities as you learn more about your market and users.

However, since hypothesis-driven development often lacks long-term cohesiveness, focus, and sustainable product experience, it’s rarely a good long-term approach to product development.

I tend to stick to outcome-driven and feature-driven approaches most of the time and resort to hypothesis-driven development if the ambiguity in a particular area is so hard that it becomes challenging to plan sensibly.

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Hypothesis testing & software development, 4. hypothesis testing & software development ¶.

Understand the concepts and processes of hypothesis testing.

Use hypothesis testing to evaluate and interpret the performance of machine learning models.

Understand the software development process and reproducibility.

Learn to use GitHub for version control and collaboration.

Expected time to complete : 3 hours

Machine learning is a powerful tool for decision making and scientific discovery. However, the results of machine learning models are often not as straightforward as it seems to interpret, and we need to be careful when using machine learning models to make decisions or draw conclusions. For example, a model may have a high accuracy but it may not be clear whether the model is actually useful.

We typically develop software to implement the machine learning model. Different software development processes can lead to different results and costs. We need to be aware of the software development process and the reproducibility of the results. Moreover, nowadays, software development is often a collaborative process. We need to learn how to collaborate with others in software development and also ensure that the software is reproducible and maintainable.

In this chapter, we will learn about the concepts and processes of hypothesis testing and how to use hypothesis testing to evaluate and interpret the performance of machine learning models. We will also learn about the software development process and reproducibility, and how to use GitHub for version control and collaboration.

Process transparency: hypothesis testing

Starting point : One or multiple groups of data to make a decision or draw a conclusion

Define the null hypothesis and the alternative hypothesis

Compute a test statistic to summarise the strength of evidence against the null hypothesis

Compute a \(p\) -value to quantify the strength of evidence against the null hypothesis

Decide whether to reject the null hypothesis or not based on a chosen significance level

End point : Report the decision or conclusion

Process transparency: software development

Starting point : A problem to solve

Initiate the project with rationale, scope, and vision

Define the project’s objectives and requirements

Build the software

Test and evaluate the software in a controlled environment

Deploy the software in a production environment

End point : Deployed software working as expected

3.3. Quiz and summary

4.1. Hypothesis testing

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Hypothesis-Driven Development

Hypothesis-driven development (hdd), also known as hypothesis-driven product development, is an approach used in software development and product management..

HDD involves creating hypotheses about user behavior, needs, or desired outcomes, and then designing and implementing experiments to validate or invalidate those hypotheses.

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Why use a hypothesis-driven approach?

With hypothesis-driven development, instead of making assumptions and building products or features based on those assumptions, teams should formulate hypotheses and conduct experiments to gather data and insights.

This method assists with making informed decisions and reduces the overall risk of building products that do not meet user needs or solve their problems.

How do you implement hypothesis-driven development

At a high level, here’s a general approach to implementing HDD:

• Identify the problem or opportunity: Begin by identifying the problem or opportunity that you want to address with your product or feature.
• Create a hypothesis: Clearly define a hypothesis that describes a specific user behavior, need, or outcome you believe will occur if you implement the solution.
• Design an experiment: Determine the best way to test your hypothesis. This could involve creating a prototype, conducting user interviews, A/B testing, or other forms of user research.
• Implement the experiment: Execute the experiment by building the necessary components or conducting the research activities.
• Collect and analyze data: Gather data from the experiment and analyze the results to determine if the hypothesis is supported or not.
• If the hypothesis is supported, you can move forward with further development.
• If the hypothesis is not supported, you may need to pivot, refine the hypothesis, or explore alternative solutions.
• Rinse and repeat: Continuously repeat the process, iterating and refining your hypotheses and experiments to guide the development of your product or feature.

Hypothesis-driven development emphasizes a data-driven and iterative approach to product development, allowing teams to make more informed decisions, validate assumptions, and ultimately deliver products that better meet user needs.

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Mastering Hypothesis Testing: A Comprehensive Guide for Researchers, Data Analysts and Data Scientists

Nilimesh Halder, PhD

Analyst’s corner

Article Outline

1. Introduction to Hypothesis Testing - Definition and significance in research and data analysis. - Brief historical background.

2. Fundamentals of Hypothesis Testing - Null and Alternative Hypothesis: Definitions and examples. - Types of Errors: Type I and Type II errors with examples.

3. The Process of Hypothesis Testing - Step-by-step guide: From defining hypotheses to decision making. - Examples to illustrate each step.

4. Statistical Tests in Hypothesis Testing - Overview of different statistical tests (t-test, chi-square test, ANOVA, etc.). - Criteria for selecting the appropriate test.

5. P-Values and Significance Levels - Understanding P-values: Definition and interpretation. - Significance Levels: Explaining alpha values and their implications.

6. Common Misconceptions and Mistakes in Hypothesis Testing - Addressing misconceptions about p-values and…

Written by Nilimesh Halder, PhD

Principal Analytics Specialist - AI, Analytics & Data Science ( https://nilimesh.substack.com/ ). Find my PDF articles at https://nilimesh.gumroad.com/l/bkmdgt

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Hypothesis Testing – A Deep Dive into Hypothesis Testing, The Backbone of Statistical Inference

• September 21, 2023

Explore the intricacies of hypothesis testing, a cornerstone of statistical analysis. Dive into methods, interpretations, and applications for making data-driven decisions.

In this Blog post we will learn:

• What is Hypothesis Testing?
• Steps in Hypothesis Testing 2.1. Set up Hypotheses: Null and Alternative 2.2. Choose a Significance Level (α) 2.3. Calculate a test statistic and P-Value 2.4. Make a Decision
• Example : Testing a new drug.
• Example in python

1. What is Hypothesis Testing?

In simple terms, hypothesis testing is a method used to make decisions or inferences about population parameters based on sample data. Imagine being handed a dice and asked if it’s biased. By rolling it a few times and analyzing the outcomes, you’d be engaging in the essence of hypothesis testing.

Think of hypothesis testing as the scientific method of the statistics world. Suppose you hear claims like “This new drug works wonders!” or “Our new website design boosts sales.” How do you know if these statements hold water? Enter hypothesis testing.

2. Steps in Hypothesis Testing

• Set up Hypotheses : Begin with a null hypothesis (H0) and an alternative hypothesis (Ha).
• Choose a Significance Level (α) : Typically 0.05, this is the probability of rejecting the null hypothesis when it’s actually true. Think of it as the chance of accusing an innocent person.
• Calculate Test statistic and P-Value : Gather evidence (data) and calculate a test statistic.
• p-value : This is the probability of observing the data, given that the null hypothesis is true. A small p-value (typically ≤ 0.05) suggests the data is inconsistent with the null hypothesis.
• Decision Rule : If the p-value is less than or equal to α, you reject the null hypothesis in favor of the alternative.

2.1. Set up Hypotheses: Null and Alternative

Before diving into testing, we must formulate hypotheses. The null hypothesis (H0) represents the default assumption, while the alternative hypothesis (H1) challenges it.

For instance, in drug testing, H0 : “The new drug is no better than the existing one,” H1 : “The new drug is superior .”

2.2. Choose a Significance Level (α)

When You collect and analyze data to test H0 and H1 hypotheses. Based on your analysis, you decide whether to reject the null hypothesis in favor of the alternative, or fail to reject / Accept the null hypothesis.

The significance level, often denoted by $α$, represents the probability of rejecting the null hypothesis when it is actually true.

In other words, it’s the risk you’re willing to take of making a Type I error (false positive).

Type I Error (False Positive) :

• Symbolized by the Greek letter alpha (α).
• Occurs when you incorrectly reject a true null hypothesis . In other words, you conclude that there is an effect or difference when, in reality, there isn’t.
• The probability of making a Type I error is denoted by the significance level of a test. Commonly, tests are conducted at the 0.05 significance level , which means there’s a 5% chance of making a Type I error .
• Commonly used significance levels are 0.01, 0.05, and 0.10, but the choice depends on the context of the study and the level of risk one is willing to accept.

Example : If a drug is not effective (truth), but a clinical trial incorrectly concludes that it is effective (based on the sample data), then a Type I error has occurred.

Type II Error (False Negative) :

• Symbolized by the Greek letter beta (β).
• Occurs when you accept a false null hypothesis . This means you conclude there is no effect or difference when, in reality, there is.
• The probability of making a Type II error is denoted by β. The power of a test (1 – β) represents the probability of correctly rejecting a false null hypothesis.

Example : If a drug is effective (truth), but a clinical trial incorrectly concludes that it is not effective (based on the sample data), then a Type II error has occurred.

Balancing the Errors :

In practice, there’s a trade-off between Type I and Type II errors. Reducing the risk of one typically increases the risk of the other. For example, if you want to decrease the probability of a Type I error (by setting a lower significance level), you might increase the probability of a Type II error unless you compensate by collecting more data or making other adjustments.

It’s essential to understand the consequences of both types of errors in any given context. In some situations, a Type I error might be more severe, while in others, a Type II error might be of greater concern. This understanding guides researchers in designing their experiments and choosing appropriate significance levels.

2.3. Calculate a test statistic and P-Value

Test statistic : A test statistic is a single number that helps us understand how far our sample data is from what we’d expect under a null hypothesis (a basic assumption we’re trying to test against). Generally, the larger the test statistic, the more evidence we have against our null hypothesis. It helps us decide whether the differences we observe in our data are due to random chance or if there’s an actual effect.

P-value : The P-value tells us how likely we would get our observed results (or something more extreme) if the null hypothesis were true. It’s a value between 0 and 1. – A smaller P-value (typically below 0.05) means that the observation is rare under the null hypothesis, so we might reject the null hypothesis. – A larger P-value suggests that what we observed could easily happen by random chance, so we might not reject the null hypothesis.

2.4. Make a Decision

Relationship between $α$ and P-Value

When conducting a hypothesis test:

We then calculate the p-value from our sample data and the test statistic.

Finally, we compare the p-value to our chosen $α$:

• If $p−value≤α$: We reject the null hypothesis in favor of the alternative hypothesis. The result is said to be statistically significant.
• If $p−value>α$: We fail to reject the null hypothesis. There isn’t enough statistical evidence to support the alternative hypothesis.

3. Example : Testing a new drug.

Imagine we are investigating whether a new drug is effective at treating headaches faster than drug B.

Setting Up the Experiment : You gather 100 people who suffer from headaches. Half of them (50 people) are given the new drug (let’s call this the ‘Drug Group’), and the other half are given a sugar pill, which doesn’t contain any medication.

• Set up Hypotheses : Before starting, you make a prediction:
• Null Hypothesis (H0): The new drug has no effect. Any difference in healing time between the two groups is just due to random chance.
• Alternative Hypothesis (H1): The new drug does have an effect. The difference in healing time between the two groups is significant and not just by chance.

Calculate Test statistic and P-Value : After the experiment, you analyze the data. The “test statistic” is a number that helps you understand the difference between the two groups in terms of standard units.

For instance, let’s say:

• The average healing time in the Drug Group is 2 hours.
• The average healing time in the Placebo Group is 3 hours.

The test statistic helps you understand how significant this 1-hour difference is. If the groups are large and the spread of healing times in each group is small, then this difference might be significant. But if there’s a huge variation in healing times, the 1-hour difference might not be so special.

Imagine the P-value as answering this question: “If the new drug had NO real effect, what’s the probability that I’d see a difference as extreme (or more extreme) as the one I found, just by random chance?”

For instance:

• P-value of 0.01 means there’s a 1% chance that the observed difference (or a more extreme difference) would occur if the drug had no effect. That’s pretty rare, so we might consider the drug effective.
• P-value of 0.5 means there’s a 50% chance you’d see this difference just by chance. That’s pretty high, so we might not be convinced the drug is doing much.
• If the P-value is less than ($α$) 0.05: the results are “statistically significant,” and they might reject the null hypothesis , believing the new drug has an effect.
• If the P-value is greater than ($α$) 0.05: the results are not statistically significant, and they don’t reject the null hypothesis , remaining unsure if the drug has a genuine effect.

4. Example in python

For simplicity, let’s say we’re using a t-test (common for comparing means). Let’s dive into Python:

Making a Decision : “The results are statistically significant! p-value < 0.05 , The drug seems to have an effect!” If not, we’d say, “Looks like the drug isn’t as miraculous as we thought.”

5. Conclusion

Hypothesis testing is an indispensable tool in data science, allowing us to make data-driven decisions with confidence. By understanding its principles, conducting tests properly, and considering real-world applications, you can harness the power of hypothesis testing to unlock valuable insights from your data.

More Articles

Correlation – connecting the dots, the role of correlation in data analysis, sampling and sampling distributions – a comprehensive guide on sampling and sampling distributions, law of large numbers – a deep dive into the world of statistics, central limit theorem – a deep dive into central limit theorem and its significance in statistics, skewness and kurtosis – peaks and tails, understanding data through skewness and kurtosis”, similar articles, complete introduction to linear regression in r, how to implement common statistical significance tests and find the p value, logistic regression – a complete tutorial with examples in r.

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A Beginner’s Guide to Hypothesis Testing in Business

• 30 Mar 2021

Becoming a more data-driven decision-maker can bring several benefits to your organization, enabling you to identify new opportunities to pursue and threats to abate. Rather than allowing subjective thinking to guide your business strategy, backing your decisions with data can empower your company to become more innovative and, ultimately, profitable.

If you’re new to data-driven decision-making, you might be wondering how data translates into business strategy. The answer lies in generating a hypothesis and verifying or rejecting it based on what various forms of data tell you.

Below is a look at hypothesis testing and the role it plays in helping businesses become more data-driven.

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What Is Hypothesis Testing?

To understand what hypothesis testing is, it’s important first to understand what a hypothesis is.

A hypothesis or hypothesis statement seeks to explain why something has happened, or what might happen, under certain conditions. It can also be used to understand how different variables relate to each other. Hypotheses are often written as if-then statements; for example, “If this happens, then this will happen.”

Hypothesis testing , then, is a statistical means of testing an assumption stated in a hypothesis. While the specific methodology leveraged depends on the nature of the hypothesis and data available, hypothesis testing typically uses sample data to extrapolate insights about a larger population.

Hypothesis Testing in Business

When it comes to data-driven decision-making, there’s a certain amount of risk that can mislead a professional. This could be due to flawed thinking or observations, incomplete or inaccurate data , or the presence of unknown variables. The danger in this is that, if major strategic decisions are made based on flawed insights, it can lead to wasted resources, missed opportunities, and catastrophic outcomes.

The real value of hypothesis testing in business is that it allows professionals to test their theories and assumptions before putting them into action. This essentially allows an organization to verify its analysis is correct before committing resources to implement a broader strategy.

As one example, consider a company that wishes to launch a new marketing campaign to revitalize sales during a slow period. Doing so could be an incredibly expensive endeavor, depending on the campaign’s size and complexity. The company, therefore, may wish to test the campaign on a smaller scale to understand how it will perform.

In this example, the hypothesis that’s being tested would fall along the lines of: “If the company launches a new marketing campaign, then it will translate into an increase in sales.” It may even be possible to quantify how much of a lift in sales the company expects to see from the effort. Pending the results of the pilot campaign, the business would then know whether it makes sense to roll it out more broadly.

Related: 9 Fundamental Data Science Skills for Business Professionals

Key Considerations for Hypothesis Testing

1. alternative hypothesis and null hypothesis.

In hypothesis testing, the hypothesis that’s being tested is known as the alternative hypothesis . Often, it’s expressed as a correlation or statistical relationship between variables. The null hypothesis , on the other hand, is a statement that’s meant to show there’s no statistical relationship between the variables being tested. It’s typically the exact opposite of whatever is stated in the alternative hypothesis.

For example, consider a company’s leadership team that historically and reliably sees $12 million in monthly revenue. They want to understand if reducing the price of their services will attract more customers and, in turn, increase revenue.

In this case, the alternative hypothesis may take the form of a statement such as: “If we reduce the price of our flagship service by five percent, then we’ll see an increase in sales and realize revenues greater than $12 million in the next month.”

The null hypothesis, on the other hand, would indicate that revenues wouldn’t increase from the base of $12 million, or might even decrease.

Check out the video below about the difference between an alternative and a null hypothesis, and subscribe to our YouTube channel for more explainer content.

2. Significance Level and P-Value

Statistically speaking, if you were to run the same scenario 100 times, you’d likely receive somewhat different results each time. If you were to plot these results in a distribution plot, you’d see the most likely outcome is at the tallest point in the graph, with less likely outcomes falling to the right and left of that point.

With this in mind, imagine you’ve completed your hypothesis test and have your results, which indicate there may be a correlation between the variables you were testing. To understand your results' significance, you’ll need to identify a p-value for the test, which helps note how confident you are in the test results.

In statistics, the p-value depicts the probability that, assuming the null hypothesis is correct, you might still observe results that are at least as extreme as the results of your hypothesis test. The smaller the p-value, the more likely the alternative hypothesis is correct, and the greater the significance of your results.

3. One-Sided vs. Two-Sided Testing

When it’s time to test your hypothesis, it’s important to leverage the correct testing method. The two most common hypothesis testing methods are one-sided and two-sided tests , or one-tailed and two-tailed tests, respectively.

Typically, you’d leverage a one-sided test when you have a strong conviction about the direction of change you expect to see due to your hypothesis test. You’d leverage a two-sided test when you’re less confident in the direction of change.

4. Sampling

To perform hypothesis testing in the first place, you need to collect a sample of data to be analyzed. Depending on the question you’re seeking to answer or investigate, you might collect samples through surveys, observational studies, or experiments.

A survey involves asking a series of questions to a random population sample and recording self-reported responses.

Observational studies involve a researcher observing a sample population and collecting data as it occurs naturally, without intervention.

Finally, an experiment involves dividing a sample into multiple groups, one of which acts as the control group. For each non-control group, the variable being studied is manipulated to determine how the data collected differs from that of the control group.

Learn How to Perform Hypothesis Testing

Hypothesis testing is a complex process involving different moving pieces that can allow an organization to effectively leverage its data and inform strategic decisions.

If you’re interested in better understanding hypothesis testing and the role it can play within your organization, one option is to complete a course that focuses on the process. Doing so can lay the statistical and analytical foundation you need to succeed.

Do you want to learn more about hypothesis testing? Explore Business Analytics —one of our online business essentials courses —and download our Beginner’s Guide to Data & Analytics .

About the Author

Why hypothesis-driven development is key to DevOps

Opensource.com

The definition of DevOps, offered by  Donovan Brown is  "The union of people , process , and products to enable continuous delivery of value to our customers. " It accentuates the importance of continuous delivery of value. Let's discuss how experimentation is at the heart of modern development practices.

Reflecting on the past

Before we get into hypothesis-driven development, let's quickly review how we deliver value using waterfall, agile, deployment rings, and feature flags.

In the days of waterfall , we had predictable and process-driven delivery. However, we only delivered value towards the end of the development lifecycle, often failing late as the solution drifted from the original requirements, or our killer features were outdated by the time we finally shipped.

Here, we have one release X and eight features, which are all deployed and exposed to the patiently waiting user. We are continuously delivering value—but with a typical release cadence of six months to two years, the value of the features declines as the world continues to move on . It worked well enough when there was time to plan and a lower expectation to react to more immediate needs.

The introduction of agile allowed us to create and respond to change so we could continuously deliver working software, sense, learn, and respond.

Now, we have three releases: X.1, X.2, and X.3. After the X.1 release, we improved feature 3 based on feedback and re-deployed it in release X.3. This is a simple example of delivering features more often, focused on working software, and responding to user feedback. We are on the path of continuous delivery, focused on our key stakeholders: our users.

Using deployment rings and/or feature flags , we can decouple release deployment and feature exposure, down to the individual user, to control the exposure—the blast radius—of features. We can conduct experiments; progressively expose, test, enable, and hide features; fine-tune releases, and continuously pivot on learnings and feedback.

When we add feature flags to the previous workflow, we can toggle features to be ON (enabled and exposed) or OFF (hidden).

Here, feature flags for features 2, 4, and 8 are OFF, which results in the user being exposed to fewer of the features. All features have been deployed but are not exposed (yet). We can fine-tune the features (value) of each release after deploying to production.

Ring-based deployment limits the impact (blast) on users while we gradually deploy and evaluate one or more features through observation. Rings allow us to deploy features progressively and have multiple releases (v1, v1.1, and v1.2) running in parallel.

Exposing features in the canary and early-adopter rings enables us to evaluate features without the risk of an all-or-nothing big-bang deployment.

Feature flags decouple release deployment and feature exposure. You "flip the flag" to expose a new feature, perform an emergency rollback by resetting the flag, use rules to hide features, and allow users to toggle preview features.

When you combine deployment rings and feature flags, you can progressively deploy a release through rings and use feature flags to fine-tune the deployed release.

See deploying new releases: Feature flags or rings , what's the cost of feature flags , and breaking down walls between people, process, and products for discussions on feature flags, deployment rings, and related topics.

Adding hypothesis-driven development to the mix

Hypothesis-driven development is based on a series of experiments to validate or disprove a hypothesis in a complex problem domain where we have unknown-unknowns. We want to find viable ideas or fail fast. Instead of developing a monolithic solution and performing a big-bang release, we iterate through hypotheses, evaluating how features perform and, most importantly, how and if customers use them.

Template: We believe {customer/business segment} wants {product/feature/service} because {value proposition}. Example: We believe that users want to be able to select different themes because it will result in improved user satisfaction. We expect 50% or more users to select a non-default theme and to see a 5% increase in user engagement.

Every experiment must be based on a hypothesis, have a measurable conclusion, and contribute to feature and overall product learning. For each experiment, consider these steps:

• Observe your user
• Define a hypothesis and an experiment to assess the hypothesis
• Define clear success criteria (e.g., a 5% increase in user engagement)
• Run the experiment
• Evaluate the results and either accept or reject the hypothesis

Let's have another look at our sample release with eight hypothetical features.

When we deploy each feature, we can observe user behavior and feedback, and prove or disprove the hypothesis that motivated the deployment. As you can see, the experiment fails for features 2 and 6, allowing us to fail-fast and remove them from the solution. We do not want to carry waste that is not delivering value or delighting our users! The experiment for feature 3 is inconclusive, so we adapt the feature, repeat the experiment, and perform A/B testing in Release X.2. Based on observations, we identify the variant feature 3.2 as the winner and re-deploy in release X.3. We only expose the features that passed the experiment and satisfy the users.

Hypothesis-driven development lights up progressive exposure

When we combine hypothesis-driven development with progressive exposure strategies, we can vertically slice our solution, incrementally delivering on our long-term vision. With each slice, we progressively expose experiments, enable features that delight our users and hide those that did not make the cut.

But there is more. When we embrace hypothesis-driven development, we can learn how technology works together, or not, and what our customers need and want. We also complement the test-driven development (TDD) principle. TDD encourages us to write the test first (hypothesis), then confirm our features are correct (experiment), and succeed or fail the test (evaluate). It is all about quality and delighting our users , as outlined in principles 1, 3, and 7  of the Agile Manifesto :

• Our highest priority is to satisfy the customers through early and continuous delivery of value.
• Deliver software often, from a couple of weeks to a couple of months, with a preference to the shorter timescale.
• Working software is the primary measure of progress.

More importantly, we introduce a new mindset that breaks down the walls between development, business, and operations to view, design, develop, deliver, and observe our solution in an iterative series of experiments, adopting features based on scientific analysis, user behavior, and feedback in production. We can evolve our solutions in thin slices through observation and learning in production, a luxury that other engineering disciplines, such as aerospace or civil engineering, can only dream of.

The good news is that hypothesis-driven development supports the empirical process theory and its three pillars: Transparency , Inspection , and Adaption .

But there is more. Based on lean principles, we must pivot or persevere after we measure and inspect the feedback. Using feature toggles in conjunction with hypothesis-driven development, we get the best of both worlds, as well as the ability to use A|B testing to make decisions on feedback, such as likes/dislikes and value/waste.

Hypothesis-driven development:

• Is about a series of experiments to confirm or disprove a hypothesis. Identify value!
• Delivers a measurable conclusion and enables continued learning.
• Enables continuous feedback from the key stakeholder—the user—to understand the unknown-unknowns!
• Enables us to understand the evolving landscape into which we progressively expose value.

Progressive exposure:

• Is not an excuse to hide non-production-ready code. Always ship quality!
• Is about deploying a release of features through rings in production. Limit blast radius!
• Is about enabling or disabling features in production. Fine-tune release values!
• Relies on circuit breakers to protect the infrastructure from implications of progressive exposure. Observe, sense, act!

What have you learned about progressive exposure strategies and hypothesis-driven development? We look forward to your candid feedback.

Comments are closed.

Related content.

• Hypothesis Driven Development – an experimentation approach to application development.!
• AnAr Processes
• Application Maintenance

In the realm of software development, innovation is the cornerstone of progress. One method that has proven highly effective in driving innovation is Hypothesis Driven Development. This approach is founded on a systematic process of observation, testing, and applied learning. It leverages the outcomes of previous experiments to inform and refine new ideas, making it a cornerstone of progress in the software industry.

In this blog, we will delve into the concept of Hypothesis Driven Development, exploring its significance and the compelling need for its adoption. We will examine how this approach addresses the challenges posed by rapidly changing technological landscapes, rising customer expectations, and fierce competition. Additionally, we will outline the framework and basis that underpin this method, providing a solid foundation for its implementation.

Need for Hypothesis Driven Development:

• Expedited delivery of new features to users.
• Addressing heightened customer demands for superior solutions.
• Maneuvering through a competitive landscape with a multitude of similar apps, emphasizing the need for quality enhancement.
• Open visibility of app comparisons and ratings, which directly impact user acquisition and retention.
• Traditional documentation and requirement analysis struggle to keep pace with the dynamic landscape of modern software development.
• Ensuring alignment with business objectives, usability standards, and stakeholder expectations, transcending mere documentation.
• Meeting the imperative for robust, enduring solutions, benefiting both service providers and consumers.
• Hypotheses serve as dynamic alternatives to traditional requirements, enabling swifter delivery.
• Adding value and fostering growth, facilitating business scalability.
• Identifying and eliminating mismatches between requirements and offerings to prevent failure.
• Establishing clear objectives, setting milestones, and employing measurable metrics.
• Providing clarity and visibility throughout the development process.
• Expanding horizons and exploring innovative approaches to software development.
• Preventing the development of superfluous or inefficient solutions.
• Implementing proven scientific methodologies to instill reliability in software development.
• Ensuring a seamless flow of feature releases.
• Adapting features based on user feedback and scheduling re-releases accordingly.
• Accelerating innovation by offering straightforward solutions to complex challenges.
• Encouraging a continuous process of formulating and testing new hypotheses.
• Instigating organizational shifts that foster trust and confidence.
• Promoting enhanced teamwork and communication among developers.
• Facilitating the creation of innovative business models.
• Enabling the formulation and testing of multiple hypotheses.
• Monitoring the stages of experiment preparation and active validation to gauge outcomes.
• Prioritizing user satisfaction over developer beliefs, leading to refined solutions.
• Employing tried-and-tested methodologies for incremental hypothesis execution, setting industry benchmarks.
• Leveraging a scientific approach to software development for enduring and impactful results.

Basis for Hypothesis Driven Development:

• Every opportunity is a learning opportunity
• Assemble data to validate the hypothesis
• Understand the user and the way they use certain software
• Refine the ideas that once seemed wonderful
• Brilliant use customer feedback to inculcate learning
• Making use of user stories as inspiration in product development
• Form the hypothesis that is flexible
• Test heading towards improvement, discover new possibilities
• Create a culture of operating by utilizing the hypothesis from the knowledge base
• Footing towards measuring what matters, define actionable metrics
• Run and evaluate a defined hypothesis
• Accept or reject the hypothesis

Is practicing Hypothesis Driven Development difficult?

Not if the metrics are established, criteria of success and failure are set, how you make decisions, clarity on why are you experimenting, the usability of feature to judge the need and precise use. If your highest priority is customer satisfaction, you will select the Hypothesis Driven Development method and deliver value enclosed software solutions.

Eliminate the barriers between the sales, customer representatives, and developers with transparency and added levels of inspection. The series of experiments allows you to gather feedback and improve immediately. Awareness of pitfalls and succeeding codes in an organized system for research makes the hypothesis better. At times, when the hypothesis fails to prove, we are still left with valuable insights.

Hypothesis Framework:

Traditional and Behaviour Driven Developmen t both have failed to a great extent due to their limitations of goal setting and delivering benefits. Hypothesis includes the proven steps to achieve the desired outcome.

A structure that supports the Hypothesis Driven Development is belief, outcome and measurable. It lets you define the test capability of the product or service by identifying the functionality we will develop to test the hypothesis, expected outcome of the experiment, and the signals that confide the capability.

Hypothesis-driven development lights up progressive exposure

When we combine hypothesis-driven development with progressive exposure strategies, we can vertically slice our solution, incrementally delivering on our long-term vision. With each slice, we progressively expose experiments, enable features that delight our users and hide those that did not make the cut.

But there is more. When we embrace hypothesis-driven development, we can learn how technology works together, or not, and what our customers need and want. We also complement the test-driven development (TDD) principle. TDD encourages us to write the test first (hypothesis), then confirm our features are correct (experiment), and succeed or fail the test (evaluate). It is all about quality and delighting our users , as outlined in principles 1, 3, and 7  of the Agile Manifesto .

Examples of Hypothesis Driven Development (HDD):

Problem Statement: Our team observed the hotel website is updated, the content is up to the mark and relevant, the services and feedback are good still the online registrations are lesser compared to walk-ins.

Missing Element: We found the images on the website are insufficient, unclear, and not the latest. In addition, its size and quality is an issue.

HDD Statement:

• Required Action: Replacing existing images with quality images, large and add noticeable images on the booking page balanced to combine with the content
• Expected Outcome: Improved customer engagement, easy search, establishing relationships with the content and higher conversion
• Experiment Criteria: When we see, about 8% increase in booking from site visitors who review the improved website and hotel images then complete the booking in 2 days as a measurable signal

Problem Statement: Our team created a landing page to encourage customers to try our new mobile app. Visitors on the page check the details yet do not download and visit other pages without leaving any comment.

Missing Element: Multiple links to download the mobile app is confusing, there are broken links, and the page design is not appealing.

• Required Action: Replace existing links, redesigning the page, add the feedback section for not downloading the app in spite of spending time on the page and improving the display
• Expected Outcome:  Increased number of mobile app downloads, more mobile app users and gather customer feedback
• Experiment Criteria: When we see an increase in clicks on mobile app link of Google Play and other links from the site, 3% customers opt for paid premium features and we have no complaints on the functioning of the links as a measurable signal

Problem statement: Our team found that traffic from social media sites is negligible on our event management company website, even when our existing customers and target/ prospects are active online.

Missing Element: The content on social media did not have any backlinks to our website,  the pages which were linked no more exists or are modified when company website was upgraded and our content was not easy to share on other platforms.

• Required Action: Adding correct back links, allowing easy share for the content, and making our social media page interesting
• Expected Outcome: Increased activity of our customer’s on social media platforms, frequent sharing of content to rise sales and help in branding
• Experiment Criteria: When we see traffic from social media has increased by 5% and an increase in bookings by 2% in a month as a measurable signal.

Experiment Criteria defines the success and relates to the hypothesis and its strong concept that results in better execution in developing new or updating existing software.

Hypothesis Driven Development is the catalyst for progress in software development. By leveraging past experiments, it refines ideas and accelerates innovation. This approach is crucial for meeting evolving customer expectations, outperforming competitors, and delivering enduring solutions. Embracing it ensures accountability, purpose, and high standards, making it an indispensable tool for software engineering.

Discover the common challenges of distributed development and learn strategies to build resilient and effective remote teams.

Discover how Retrieval-Augmented Generation (RAG) revolutionizes AI by ensuring accurate and contextual responses for enterprises.

Choose wisely between digital improvement, which optimizes existing systems, and digital transformation, which completely overhauls your approach for innovation. Discover the key differences and decide what’s best for your business.

Effective leadership is the cornerstone of successful digital transformation. As organizations navigate the shift to digital platforms, leaders must not only manage technological upgrades but also guide their teams through significant changes.

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Scrum and Hypothesis Driven Development

• Website for Dave West
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• LinkedIn for Dave West

Scrum was built to better manage risk and deliver value by focusing on inspection and encouraging adaptation. It uses an empirical approach combined with self organizing, empowered teams to effectively work on complex problems. And after reading Jeff Gothelf ’s and Josh Seiden ’s book “ Sense and Respond: How Successful Organizations Listen to Customers and Create New Products Continuously ”, I realized that the world is full of complex problems. This got me thinking about the relationship between Scrum and modern organizations as they pivot toward becoming able to ‘sense and respond’. So, I decided to ask Jeff Gothelf… Here is a condensed version of our conversation.

Sense & Respond was exactly this attempt to change the hearts and minds of managers, executives and aspiring managers. It makes the case that first and foremost, any business of scale or that seeks to scale is in the software business. We share a series of compelling case studies to illustrate how this is true across nearly every industry. We then move on to the second half of the book where we discuss how managing a software-based business is different. We cover culture, process, staffing, planning, budgeting and incentives. Change has to be holistic.

What you are describing is the challenge of ownership. Product Owner (PO) is the role in the Scrum Framework empowered to make decisions about what and when things are in the product. But disempowerment is a real problem in most organizations, with their POs not having the power to make decisions. Is this something you see when introducing the ideas of Sense and Respond?

There will always be situations where things simply have to get built. Legal and compliance are two great examples of this. In these, low risk, low uncertainty situations a more straightforward execution is usually warranted. That said, just because a feature has to be included for compliance reasons doesn’t mean there is only one way to implement it. What teams will often find is that there is actual flexibility in how these (actual) requirements can be implemented with some being more successful and less distracting to the overall user experience than others. The level of discovery that you would expend on these features is admittedly smaller but it shouldn’t be thrown out altogether as these features still need to figure into a holistic workflow.   

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• Knowledge Base

Hypothesis Testing | A Step-by-Step Guide with Easy Examples

Published on November 8, 2019 by Rebecca Bevans . Revised on June 22, 2023.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics . It is most often used by scientists to test specific predictions, called hypotheses, that arise from theories.

There are 5 main steps in hypothesis testing:

• State your research hypothesis as a null hypothesis and alternate hypothesis (H o ) and (H a  or H 1 ).
• Collect data in a way designed to test the hypothesis.
• Perform an appropriate statistical test .
• Decide whether to reject or fail to reject your null hypothesis.
• Present the findings in your results and discussion section.

Though the specific details might vary, the procedure you will use when testing a hypothesis will always follow some version of these steps.

Table of contents

Step 1: state your null and alternate hypothesis, step 2: collect data, step 3: perform a statistical test, step 4: decide whether to reject or fail to reject your null hypothesis, step 5: present your findings, other interesting articles, frequently asked questions about hypothesis testing.

After developing your initial research hypothesis (the prediction that you want to investigate), it is important to restate it as a null (H o ) and alternate (H a ) hypothesis so that you can test it mathematically.

The alternate hypothesis is usually your initial hypothesis that predicts a relationship between variables. The null hypothesis is a prediction of no relationship between the variables you are interested in.

• H 0 : Men are, on average, not taller than women. H a : Men are, on average, taller than women.

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For a statistical test to be valid , it is important to perform sampling and collect data in a way that is designed to test your hypothesis. If your data are not representative, then you cannot make statistical inferences about the population you are interested in.

There are a variety of statistical tests available, but they are all based on the comparison of within-group variance (how spread out the data is within a category) versus between-group variance (how different the categories are from one another).

If the between-group variance is large enough that there is little or no overlap between groups, then your statistical test will reflect that by showing a low p -value . This means it is unlikely that the differences between these groups came about by chance.

Alternatively, if there is high within-group variance and low between-group variance, then your statistical test will reflect that with a high p -value. This means it is likely that any difference you measure between groups is due to chance.

Your choice of statistical test will be based on the type of variables and the level of measurement of your collected data .

• an estimate of the difference in average height between the two groups.
• a p -value showing how likely you are to see this difference if the null hypothesis of no difference is true.

Based on the outcome of your statistical test, you will have to decide whether to reject or fail to reject your null hypothesis.

In most cases you will use the p -value generated by your statistical test to guide your decision. And in most cases, your predetermined level of significance for rejecting the null hypothesis will be 0.05 – that is, when there is a less than 5% chance that you would see these results if the null hypothesis were true.

In some cases, researchers choose a more conservative level of significance, such as 0.01 (1%). This minimizes the risk of incorrectly rejecting the null hypothesis ( Type I error ).

The results of hypothesis testing will be presented in the results and discussion sections of your research paper , dissertation or thesis .

In the results section you should give a brief summary of the data and a summary of the results of your statistical test (for example, the estimated difference between group means and associated p -value). In the discussion , you can discuss whether your initial hypothesis was supported by your results or not.

In the formal language of hypothesis testing, we talk about rejecting or failing to reject the null hypothesis. You will probably be asked to do this in your statistics assignments.

However, when presenting research results in academic papers we rarely talk this way. Instead, we go back to our alternate hypothesis (in this case, the hypothesis that men are on average taller than women) and state whether the result of our test did or did not support the alternate hypothesis.

If your null hypothesis was rejected, this result is interpreted as “supported the alternate hypothesis.”

These are superficial differences; you can see that they mean the same thing.

You might notice that we don’t say that we reject or fail to reject the alternate hypothesis . This is because hypothesis testing is not designed to prove or disprove anything. It is only designed to test whether a pattern we measure could have arisen spuriously, or by chance.

If we reject the null hypothesis based on our research (i.e., we find that it is unlikely that the pattern arose by chance), then we can say our test lends support to our hypothesis . But if the pattern does not pass our decision rule, meaning that it could have arisen by chance, then we say the test is inconsistent with our hypothesis .

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Descriptive statistics
• Measures of central tendency
• Correlation coefficient

Methodology

• Cluster sampling
• Stratified sampling
• Types of interviews
• Cohort study
• Thematic analysis

Research bias

• Implicit bias
• Cognitive bias
• Survivorship bias
• Availability heuristic
• Nonresponse bias
• Regression to the mean

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

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