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Post-Test Surveys: Definition, Elements & How to Create One

Pre- and post-test surveys are valuable tools for gathering information and measuring change over time. The pre-test survey gathers baseline information before any changes or interventions occur, providing a starting point. Then, the post-test survey comes into play, collecting follow-up data after the changes or treatments have been applied. 

This two-step approach effectively measures the impact, progress, or effectiveness of a program, course, or intervention, making it a valuable tool in education, research, and various other fields. 

These pre-tests and post-tests serve as feedback mechanisms that lead to better outcomes and informed decision-making in various fields.

In this blog, we’ll dive into the world of post-test surveys, understanding what they are, the essential elements that make them effective, and how to create one that delivers meaningful results.

What are post-test surveys?

Post-test surveys are like feedback forms you receive after you’ve finished a class, training program, or any kind of event. These surveys ask you questions about your experience, what you’ve learned, and how you felt about the whole thing.

Imagine you’ve just completed an online course. After taking all the lessons and quizzes, the course may ask you to complete a survey. In that survey, they might want to know if you found the content easy to understand if the course met your expectations, and if you have any suggestions for improvement.

The organizers or instructors use these surveys to get a sense of how well they did and where they can do better next time. It’s like getting a report card for the course or event, but you’re the one giving the grades and comments. 

The feedback from these surveys helps them make improvements for future participants to have a better experience. So, these surveys are an essential tool for learning from your experiences and making things even better in the future.

Benefits of post-test surveys

These surveys play a crucial role in various fields, from education to business, by offering a range of valuable advantages:

Collecting valuable feedback

When you finish a course or training, taking a post-test survey is like sharing your thoughts and opinions. It’s a way for you to tell the organizers what you liked or didn’t like. Your feedback is really important because it helps them understand how you felt about the whole experience. 

This way, they can make changes and improvements based on what you say. Your opinions matter and can shape how things are done in the future.

Identifying knowledge gaps

Sometimes, you might not realize what you didn’t understand until you see the post-test survey questions. These surveys can help you spot the areas where you might have missed something in the course or training. 

By highlighting these gaps in your knowledge, you get a chance to go back and review those parts or seek additional help. It’s like a map showing where to focus on learning more.

Improving course content

Your feedback in these surveys can be like a treasure map for the course creators. They can see what you enjoyed and what you didn’t. If many people say they loved a specific part of the course, it tells them to keep doing more of that. 

And if you and others suggest improvements, it gives them ideas on how to make the course even better for future learners. Your feedback guides them in creating content that suits your needs and preferences.

Enhancing user experience

Your experience matters and these surveys help make it better. When you share your thoughts, you’re helping to make sure the next person who takes the course or attends the event has an even more enjoyable time. 

Organizers use your feedback to tweak things, fix any issues, and create a smoother and more satisfying experience for everyone. So, by participating in a post-test questionnaire, you make things more user-friendly and enjoyable for others.

Key elements of an effective post-test survey

When you’re creating a post-test survey to gather valuable feedback, there are some important things to keep in mind. These key elements will help you make sure your survey is effective:

• Well-crafted question: Your question should be clear and easy to understand. This way, people can quickly and accurately answer them.
• Relevant and focused topics: Make sure your question is directly related to the event or experience you’re assessing. Avoid straying off-topic. It’s like discussing your favorite movie in a survey about a cooking class that doesn’t fit.
• Balanced question types: Use a mix of different question types. Multiple-choice questions are like choosing from a menu with options, while open-ended questions are like writing a short paragraph. This balance helps you gather both quick, quantitative data and detailed, qualitative insights.
• Adequate survey length: Think about how long it takes to complete your survey. If it’s too long, people might get tired and not finish it. If it’s too short, you might not get enough useful information.
• Proper timing: Timing is crucial. You should give the survey right after the event while things are fresh in people’s minds. It’s like taking a picture of something beautiful while it’s right in front of you, and it captures the moment accurately.
• Pre-testing: Before sending out your survey to a larger audience, test it on a small group first. This is like trying out a new recipe with a few friends before serving it at a big dinner party. Testing helps you catch any issues and make improvements.

Creating engaging post-test survey questions

Creating engaging post-test survey questions is essential for collecting meaningful feedback from participants. Engaging questions encourage respondents to provide detailed and honest survey responses , leading to valuable insights. Here are some tips on how to conduct engaging post-test survey questions for post-test-surveys:

• Start with a clear purpose: Before you begin, define the main objectives of your survey. Understand what specific information you want to gather and what decisions or improvements the survey will support.
• Keep questions clear and simple: Use plain language and straightforward wording. Avoid jargon or complex terminology that may confuse respondents. Your question should be easy to understand at a glance.
• Ask one question at a time: Avoid double-barreled questions that ask about multiple things simultaneously. Each question should focus on a single topic or aspect to ensure clear and accurate responses. 
• Use varied question types:
• Multiple-choice questions: Provide options for respondents to choose from. These questions are great for capturing quantitative data.
• Likert scale questions: Use a scale to measure agreement or satisfaction. For example, from “Strongly Disagree” to “Strongly Agree.”
• Open-ended questions: Allow respondents to provide free-text responses. These questions yield qualitative data and encourage detailed feedback.
• Balance positives and negatives: Include questions that ask about both positive and negative aspects of the event or experience. Encourage respondents to share what they liked and what they believe could be improved.
• Consider question order: Arrange your questions in a logical and user-friendly sequence. Start with easy and non-invasive questions to build respondent confidence before delving into more complex or personal topics.
• Test your questions: Before distributing the questionnaire, test it with a small group of individuals to identify any confusing or problematic questions. Their feedback can help you refine your survey.
• Provide clear instructions: If a question requires specific information or context, provide clear instructions or examples to ensure respondents understand what’s being asked.
• Ensure mobile-friendly design: Many people take questionnaires on mobile devices. Ensure that your questionnaire is responsive and easy to complete on smartphones and tablets.

Creating engaging post-test survey questions is all about making it easy and appealing for participants to provide their thoughts and insights. When you make questions that are clear, relevant, and diverse in format, you’re more likely to receive valuable feedback. It can guide improvements and decision-making based on the genuine perspectives of your respondents.

How QuestionPro helps in conducting post-test surveys?

QuestionPro provides valuable assistance in conducting post-test surveys through a range of features and capabilities:

• Ease of survey creation: QuestionPro offers an intuitive platform for creating post-test questionnaires. You can design questionnaires from scratch or use templates, simplifying the survey creation process.
• Diverse question types: The platform supports various question types, such as multiple-choice, open-ended, and rating scales, enabling you to conduct surveys that gather comprehensive feedback.
• Customization: You can personalize your questionnaires by adding your organization’s branding elements, like logos, colors, and fonts, ensuring a consistent and professional appearance.
• Mobile responsiveness: These questionnaires designed with QuestionPro are mobile-responsive, ensuring that participants can complete surveys on their preferred devices and enhancing accessibility.
• Distribution flexibility: The platform provides multiple distribution methods, allowing you to share questionnaires via email, social media, or website embedding, making it easy to reach your target audience.
• Data analysis tools: QuestionPro offers robust data analysis tools that help you interpret survey results with charts, graphs, and reports, facilitating data-driven decision-making.
• Data security and privacy: QuestionPro prioritizes data security, ensuring that participant data remains confidential and protected and building trust among survey takers.
• Integrations: If you use other tools or platforms, QuestionPro offers integrations that allow you to connect survey data with other systems, streamlining your data management.
• Flexible pricing: QuestionPro offers a range of pricing plans, from free options with basic features to paid plans with advanced functionalities, making it adaptable to different budget requirements.

QuestionPro assists in conducting post-test surveys by providing a comprehensive platform that simplifies survey creation, distribution, and data analysis. Its features help you collect, analyze, and leverage survey data effectively, whether you’re evaluating training programs, products, or other post-test survey objectives.

Post-test surveys are invaluable for evaluating the effectiveness of educational programs, training initiatives, product developments, and marketing efforts. Understanding the key elements and using a versatile survey platform like QuestionPro can greatly assist in the creation and execution of these questionnaires. 

Using QuestionPro survey software, you can gain actionable insights and continuously enhance your initiatives, driving positive change and success. Contact QuestionPro today to get the best value for your post-test surveys.

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EXPERIMENT 1 - Postlab

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The post-lab questions provided are SIMILAR to questions you may encounter in your lab manual (link). These questions will provide you with a step-by-step guide on how we approached the problem. You can use this guide and the data you recorded to help answer the questions. Any given examples with values will be with mock values. Use of these values will almost assuredly give you nonsensical calculations. They are only meant to illustrate the logic.

What was the most accurate method measuring volume in your experiment?

Note that the mass of water was determined by subtracting the mass of the beaker from the mass of the beaker with water. This is done for all instruments (graduated cylinder, pipet, and buret) used to transfer water. Using the density of water (1 g/mL), the most accurate instrument will be the one closest to the expected value.

For example 1 gram for 1 mL, 2 grams for 2 mL, etc. This should be taken from your data sheet Part C. Dispensing Liquids. The sample data shown have all three instruments delivering 0.100 grams. That means that all are of the same accuracy if 0.100 mL was delivered.

Click to enlarge

Give the precision of each instrument using numbers and their appropriate units.

The precision of an instrument will be dependent on the instrument you use and possibly the manufacturer. Look on the instrument you are using. It will sometimes give values that indicate its precision. If it is not noted, a rule of thumb is to take half the smallest gradation on the instrument (e.g., if the smallest gradation is 0.1 then ± 0.05)

Why was the instrument in question 1 most accurate (use information from question 2)?

Give your answer using the information in question 1 and 2 along with factors that may make instruments more or less accurate and/or precise. For example, the the method of transfer using the graduated cylinder may result in residual liquid not being transferred and contributing in less precision (stuck at the bottom).

About the Activities

These experiments are specifically designed to incorporate and strengthen inquiry thinking patterns, process skills (such as teamwork, experimental design, and data pooling), and reflection and application skills. the procedures are specifically built for students to get reliable data with appropriate facilitation. each activity includes pre- and post-experiment questions along with the actual experimental method to be followed during a typical three-hour lab period. as with all pogil materials, students should work in self-directed teams with the facilitator as a guide. the experiments may be used with or without a laboratory notebook and instructor materials include facilitation information and report form templates., about the authors, this group of pogil activities, edited by michael garoutte, is the result of a collaborative project with a goal of creating guided inquiry laboratory experiences with relevant connections between the experiments and broader topics of student interest. the team, all experienced in using guided inquiry in class, lab, or both, and representing a wide range of institutions, included ehren bucholtz, stacey fiddler, michael garoutte, tim herzog, ashley mahoney, rick moog, marty perry, craig teague, mary van opstal, gail webster, and rob whitnell., institutions in which the activities were tested include: cornell college (ia), franklin & marshall college (pa), guilford college (nc), harper college (il), missouri southern state university (mo), portland community college (or), st. louis college of pharmacy (mo), and weber state university (ut)., correlation of activities to chemistry courses, click on the button below for a table that helps you correlate each of the laboratory activities to various chemistry courses., description of each laboratory activity.

List of Laboratory Activities

Is this molecule 3d  isbn: 979-8-3851-1769-7, why does the can implode  isbn: 979-8-3851-1770-3, how do cold packs work  isbn: 979-8-3851-1771-0, do i need more iron in my diet  isbn: 979-8-3851-1772-7, how pure is it  isbn: 979-8-3851-1773-4, can nonmetals be magnetic  isbn: 979-8-3851-1774-1, which one runs out first  isbn: 979-8-3851-1776-5, which salts dissolve  isbn: 979-8-3851-1775-8, will it sink or float  isbn: 979-8-3851-1777-2, how slow does it flow  isbn:  979-8-3851-1778-9, do all titration curves look the same  isbn: 979-8-3851-1779-6, are we there yet  isbn: 979-8-3851-1780-2, why do we need to eat  isbn: 979-8-3851-1781-9, options for incorporating these activities into your course, these activities may be purchased as.

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Activities are priced per student:

• 1-4 activities - $5 each per student
• 5-8 activities - $4 each per student
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Pretest-Posttest Designs

For many true experimental designs , pretest-posttest designs are the preferred method to compare participant groups and measure the degree of change occurring as a result of treatments or interventions.

This article is a part of the guide:

• Experimental Research
• Third Variable
• Research Bias
• Independent Variable
• Between Subjects

Browse Full Outline

• 1 Experimental Research
• 2.1 Independent Variable
• 2.2 Dependent Variable
• 2.3 Controlled Variables
• 2.4 Third Variable
• 3.1 Control Group
• 3.2 Research Bias
• 3.3.1 Placebo Effect
• 3.3.2 Double Blind Method
• 4.1 Randomized Controlled Trials
• 4.2 Pretest-Posttest
• 4.3 Solomon Four Group
• 4.4 Between Subjects
• 4.5 Within Subject
• 4.6 Repeated Measures
• 4.7 Counterbalanced Measures
• 4.8 Matched Subjects

Pretest-posttest designs grew from the simpler posttest only designs, and address some of the issues arising with assignment bias and the allocation of participants to groups.

One example is education, where researchers want to monitor the effect of a new teaching method upon groups of children. Other areas include evaluating the effects of counseling, testing medical treatments, and measuring psychological constructs. The only stipulation is that the subjects must be randomly assigned to groups, in a true experimental design, to properly isolate and nullify any nuisance or confounding variables .

The Posttest Only Design With Non-Equivalent Control Groups

Pretest-posttest designs are an expansion of the posttest only design with nonequivalent groups, one of the simplest methods of testing the effectiveness of an intervention.

In this design, which uses two groups, one group is given the treatment and the results are gathered at the end. The control group receives no treatment, over the same period of time, but undergoes exactly the same tests.

Statistical analysis can then determine if the intervention had a significant effect . One common example of this is in medicine; one group is given a medicine, whereas the control group is given none, and this allows the researchers to determine if the drug really works. This type of design, whilst commonly using two groups, can be slightly more complex. For example, if different dosages of a medicine are tested, the design can be based around multiple groups.

Whilst this posttest only design does find many uses, it is limited in scope and contains many threats to validity . It is very poor at guarding against assignment bias , because the researcher knows nothing about the individual differences within the control group and how they may have affected the outcome. Even with randomization of the initial groups, this failure to address assignment bias means that the statistical power is weak.

The results of such a study will always be limited in scope and, resources permitting; most researchers use a more robust design, of which pretest-posttest designs are one. The posttest only design with non-equivalent groups is usually reserved for experiments performed after the fact, such as a medical researcher wishing to observe the effect of a medicine that has already been administered.

The Two Group Control Group Design

This is, by far, the simplest and most common of the pretest-posttest designs, and is a useful way of ensuring that an experiment has a strong level of internal validity . The principle behind this design is relatively simple, and involves randomly assigning subjects between two groups, a test group and a control . Both groups are pre-tested, and both are post-tested, the ultimate difference being that one group was administered the treatment.

This test allows a number of distinct analyses, giving researchers the tools to filter out experimental noise and confounding variables . The internal validity of this design is strong, because the pretest ensures that the groups are equivalent. The various analyses that can be performed upon a two-group control group pretest-posttest designs are (Fig 1):

• This design allows researchers to compare the final posttest results between the two groups, giving them an idea of the overall effectiveness of the intervention or treatment. (C)
• The researcher can see how both groups changed from pretest to posttest, whether one, both or neither improved over time. If the control group also showed a significant improvement, then the researcher must attempt to uncover the reasons behind this. (A and A1)
• The researchers can compare the scores in the two pretest groups, to ensure that the randomization process was effective. (B)

These checks evaluate the efficiency of the randomization process and also determine whether the group given the treatment showed a significant difference.

Problems With Pretest-Posttest Designs

The main problem with this design is that it improves internal validity but sacrifices external validity to do so. There is no way of judging whether the process of pre-testing actually influenced the results because there is no baseline measurement against groups that remained completely untreated. For example, children given an educational pretest may be inspired to try a little harder in their lessons, and both groups would outperform children not given a pretest, so it becomes difficult to generalize the results to encompass all children.

The other major problem, which afflicts many sociological and educational research programs, is that it is impossible and unethical to isolate all of the participants completely. If two groups of children attend the same school, it is reasonable to assume that they mix outside of lessons and share ideas, potentially contaminating the results. On the other hand, if the children are drawn from different schools to prevent this, the chance of selection bias arises, because randomization is not possible.

The two-group control group design is an exceptionally useful research method, as long as its limitations are fully understood. For extensive and particularly important research, many researchers use the Solomon four group method , a design that is more costly, but avoids many weaknesses of the simple pretest-posttest designs.

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Martyn Shuttleworth (Nov 3, 2009). Pretest-Posttest Designs. Retrieved Sep 05, 2024 from Explorable.com: https://explorable.com/pretest-posttest-designs

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Pretest-Posttest Design: Definition & Examples

A  pretest-posttest design  is an experiment in which measurements are taken on individuals both before and  after they’re involved in some treatment.

Pretest-posttest designs can be used in both experimental and quasi-experimental research and may or may not include control groups. The process for each research approach is as follows:

Quasi-Experimental Research

1. Administer a pre-test to a group of individuals and record their scores.

2.  Administer some treatment designed to change the score of individuals.

3.  Administer a post-test to the same group of individuals and record their scores.

4.  Analyze the difference between pre-test and post-test scores.

Example:  All students in a certain class take a pre-test. The teacher then uses a certain teaching technique for one week and administers a post-test of similar difficulty. She then analyzes the differences between the pre-test and post-test scores to see if the teaching technique had a significant effect on scores.

Experimental Research

1.  Randomly assign individuals to a treatment group or control group.

2. Administer the same pre-test to all individuals and record their scores.

3. Administer some treatment procedure to individuals in the treatment group and administer some standard procedure to individuals in the control group.

4. Administer the same post-test to individuals in both groups.

5. Analyze the difference between pre-test and post-test scores between the treatment group and control group.

Example:  A teacher splits randomly assigns half of her class to a control group and the other half to a treatment group. She then uses a standard teaching technique and a new teaching technique with each group respectively for one week and then administers a post-test of similar difficulty to all students. She then analyzes the differences between the pre-test and post-test scores to see if the teaching technique had a significant effect on scores between the two groups.

P otential Issues with Internal Validity

Internal validity  refers to the extent in which a study establishes a reliable cause-and-effect relationship between a treatment and an outcome.

In a pretest-posttest design experiment, there are several factors that could affect internal validity, including:

• History – Individuals experience some event outside of the study that affects the measurements before and after a treatment.
• Maturity – Biological changes in participants affect the measurements before and after a treatment.
• Attrition – An individual leaves the study before a post-measurement can be taken.
• Regression to the mean – People who score extremely high or low on some measurement have a tendency to score closer to the average next time, despite the treatment they partake in.
•   Selection bias  – The individuals in the treatment group and control group are not actually comparable.

Often random selection and random assignment of individuals to groups can minimize these threats to internal validity, but not in all cases.

Additional Resources

The following tutorials provide additional information about different types of experimental designs:

Split-Plot Design: Definition & Example Matched Pairs Design: Definition & Example Cross-Lagged Panel Design: Definition & Example

Featured Posts

Hey there. My name is Zach Bobbitt. I have a Masters of Science degree in Applied Statistics and I’ve worked on machine learning algorithms for professional businesses in both healthcare and retail. I’m passionate about statistics, machine learning, and data visualization and I created Statology to be a resource for both students and teachers alike.  My goal with this site is to help you learn statistics through using simple terms, plenty of real-world examples, and helpful illustrations.

6 Replies to “Pretest-Posttest Design: Definition & Examples”

For an experimental design, the researcher can make cause-and-effect inferences and this type of research is useful for determining whether or not a specific counseling intervention is effective. This session will provide an introduction to the basics underlying experimental design, such as reliability, validity, and replicability. Topic Outcomes: By the end of this session, students will be able to: Examine the principles of experimental research in counseling. Consider models of experimental design. Evaluate threats to internal and external validity. Explore applications of experimental research in counseling contexts.

Good day, how do we calculate the resultes?

Thanks Zach! I’m still having trouble deciding which level of measure to use with my pretest/posttest design. I am using the test for two purposes: pretest/posttest compare control /treatment student scores & comparing teacher effective instruction using students score outcomes as a factor. Am I using interval or ratio?

Hello Zach!

My research is about the effectiveness of a program I designed. I will have 2 groups: experimental and control randomly selected, and participants were randomly selected before that. I will give the pretest to the 2 groups, then teach the programme to the experimental group only (i.e. . the control group will receive no treatment) & finally give the post test to both groups. Am I on the right track?

Hello sir good evening, presently I am about to craft a pre and post-test to evaluate the students’ knowledge before the training. the problem is where do i begin and what is the format for these tests? thank you very much.

Hi Eduardo…To begin crafting a pre-test and post-test for evaluating students’ knowledge before and after training, follow these steps:

### 1. **Identify Learning Objectives:** – Clearly define the key knowledge or skills that the training aims to impart. – Each test question should align with these objectives to ensure the test accurately measures the intended outcomes.

### 2. **Designing the Test Format:** – **Multiple-Choice Questions (MCQs):** These are commonly used for assessing knowledge. Ensure each question has one correct answer and plausible distractors. – **Short Answer/Essay Questions:** Useful for assessing deeper understanding or the ability to articulate knowledge. – **True/False Questions:** Good for assessing basic understanding of key concepts. – **Matching Items:** Can be used to assess knowledge of relationships between concepts. – **Practical/Application-Based Questions:** For training that involves skills, you can include scenarios or problems that require application of the knowledge.

### 3. **Crafting the Pre-Test:** – **Purpose:** To assess the students’ baseline knowledge. – **Question Difficulty:** Questions should range from basic to more challenging to gauge the full spectrum of student understanding. – **Content:** Focus on the core concepts that will be covered in the training. Avoid including content that hasn’t been introduced yet.

### 4. **Crafting the Post-Test:** – **Purpose:** To measure what the students have learned during the training. – **Question Parity:** Ideally, the post-test should mirror the pre-test in format and content, but with different questions that assess the same concepts. – **Include Higher-Order Thinking Questions:** If applicable, include questions that require students to analyze, evaluate, or create based on the knowledge they’ve gained.

### 5. **Scoring and Analysis:** – **Consistency:** Ensure the scoring method is consistent between pre and post-tests. – **Comparison:** Analyze the difference between pre-test and post-test scores to assess the effectiveness of the training. – **Feedback:** Provide feedback to students on both tests to reinforce learning.

### 6. **Pilot Testing:** – Before administering the tests to the full group, consider piloting them with a small group to identify any issues with question clarity or difficulty.

### 7. **Implementation:** – Administer the pre-test before any instruction begins. – After the training, administer the post-test under similar conditions to ensure comparability.

### Example Format:

– **Pre-Test:** – 10 multiple-choice questions covering the basics of the training content. – 2 short-answer questions to assess prior understanding.

– **Post-Test:** – 10 multiple-choice questions that mirror the pre-test but with different phrasing or context. – 1 scenario-based question that requires application of the knowledge gained.

This structured approach will help you create effective pre and post-tests that accurately measure the impact of your training.

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• 10 Research Question Examples to Guide Your Research Project

10 Research Question Examples to Guide your Research Project

Published on October 30, 2022 by Shona McCombes . Revised on October 19, 2023.

The research question is one of the most important parts of your research paper , thesis or dissertation . It’s important to spend some time assessing and refining your question before you get started.

The exact form of your question will depend on a few things, such as the length of your project, the type of research you’re conducting, the topic , and the research problem . However, all research questions should be focused, specific, and relevant to a timely social or scholarly issue.

Once you’ve read our guide on how to write a research question , you can use these examples to craft your own.

Note that the design of your research question can depend on what method you are pursuing. Here are a few options for qualitative, quantitative, and statistical research questions.

Other interesting articles

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

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• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

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• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

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• Experiments
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Test changes with statistical significance

A/B tests, multivariate tests, and robust targeting & exclusion rules. Analyze usage with product analytics and session replay .

boosted community engagement by 40%

" Y Combinator uses PostHog's experiments to try new ideas, which has led to significant improvements. "

tests product changes for over 25M users

" Our data scientists are able to rapidly and autonomously iterate on the data models that power our home feed. "

increased registrations by 30%

" This experiment cuts drop-off in half – that's a 50% improvement without a single user complaining! "

switched from Mixpanel for a leaner stack

" I feel like, every single week, we discover something new that makes a difference. "

PostHog vs...

• Installation

Meet the team

Roadmap & changelog.

Customizable goals

Conversion funnels or trends, secondary metrics, and range for statistical significance

Targeting & exclusion rules

Set criteria for user location, person property, cohort, or group

Recommendations

Automatic suggestions for duration, sample size, and confidence threshold in a winning variant

Built on Feature Flags

All the benefits of feature flags with added functionality around stat-sig experiments

JSON payloads

Modify website content per-variant without additional deployments

Split testing

Automatically split traffic between variants

Multivariate testing

Test up to 9 variants against a control

Dynamic cohort support

Add new users to an experiment automatically by setting a person property

Answer all of these questions (and more) with PostHog Experiments .

• Does this new onboarding flow increase conversion?
• How does this affect adoption in Europe?
• Will enterprise customers like this new feature?

Usage-based pricing

Use Experiments free. Or enter a credit card for advanced features. Either way, your first 1,000,000 requests are free – every month.

No credit card required

All other plans

All features, no limitations

1,000,000 /mo

Boolean feature flags

Multivariate feature flags & experiments, persist flags across authentication, test changes without code, multiple release conditions, release condition overrides, flag targeting by groups, local evaluation & bootstrapping, flag usage stats, funnel & trend experiments, secondary experiment metrics, statistical analysis, group experiments, multi-environment support, data retention, monthly pricing, first 1 million requests, 1-2 million, $ 0.000100 / request, 2-10 million, $ 0.000045 / request, 10-50 million, $ 0.000025 / request, 50 million+, $ 0.000010 / request.

How do I know what my request volume is?

Is there a free trial on paid plans?

What currency are your prices in?

Do you offer a discount for non-profits?

Are there any minimums or annual commitments?

So, what's best for you?

Reasons a competitor may be best for you (for now...).

• You'll still need a designer/engineer to create experiments
• PostHog can't run ad experiments, or target users into an experiment based on an ad variant engagement.

Reasons to choose

• Attach surveys to experiments or view replays for a test group. Analyze results beyond your initial hypothesis or goal metric.
• Automated recommendations for sample sizes and runtime
• Automatic significance calculator – to help you figure out the winning variant as quickly as possible
• Anything you monitor in analytics, you can target in an experiment

Have questions about PostHog? Ask the community or book a demo .

Featured tutorials

Visit the tutorials section for more.

Running experiments on new users

Optimizing the initial experience of new users is critical for turning them into existing users. Products have a limited amount of time and attention from new users before they leave and churn.

How to set up A/B/n testing

A/B/n testing is like an A/B test where you compare multiple (n) variants instead of just two. It can be especially useful for small but impactful changes where many options are available like copy, styles, or pages.

How to run holdout testing

Holdout testing is a type of A/B testing that measures the long term effects of product changes. In holdout testing, a small group of users is not shown your changes for a long period of time, typically weeks or months after your experiment ends.

How to do A/A testing

An A/A test is the same as an A/B test except both groups receive the same code or components. Teams run A/A tests to ensure their A/B test service, functionality, and implementation work as expected and provides accurate results.

Install & customize

Here are some ways you can fine tune how you implement Experiments .

Setting up an experiment

Explore the docs

Get a more technical overview of how everything works in our docs .

• Creating an experiment
• Adding your code
• Testing and launching
• Troubleshooting and FAQs
• Tutorials and guides

Methodology

• Traffic allocation
• Sample size and running time
• Experiment significance
• Experiments without feature flags

PostHog works in small teams. The Feature Success team is responsible for building Experiments .

Annika Schmid

Product Manager

Believe it or not, my first job included writing exciting marketing campaigns for robotic handling systems . (It wasn’t that exciting.) So I ended up moving to London in 2019 to study for an MSc in Human-Computer Interaction at UCL. (Way more exciting.) My plan was to land a job in a startup after graduating, which, through a bit of ‘hustling’ ( Sigma Squared Summit, Voyagers, Kickstart London ) and being in the right place at the right time actually worked out.

After graduating, I joined an early-stage startup called  Caura , where I first had the title of a Product Designer and later on, that of a Product Manager. Titles aside, what motivates me most in my work is talking to users about what they want, uncovering what they actually mean by that and then building something they didn’t even think they needed.

I joined PostHog in autumn 2022, arguably the most exciting step in my career so far.

What else? On weekends, I hang out with friends, try to make a dent in my ever growing reading list or learn something new. My family is from the Black Forest region in Germany, so I spend a couple of weeks every year working from there, going on lots of countryside walks and eating Black Forest gateau.

Juraj Majerik

Full Stack Engineer

I had been building radio-controlled planes for much of my teens. This taught me precision, coming up with my own designs and having them stand the test of physics.

After three years of studying economics, I toyed with a business idea that led nowhere - except for leading me to learn web development. After a brief stint as a translator at Booking.com, I pivoted to programming in earnest and haven't looked back since.

I spent the last couple of years as a full stack engineer at a startup focusing on news analytics. I learned a ton and got to wear many hats. Among other things, I built the Alerts product and the scoring & aggregation part of the pipeline, which processes up to a million articles per day.

On the side, I'm working on a ride-sharing simulation and writing a blog . My project was featured on The Pragmatic Engineer, the #1 technology newsletter on Substack.

I love cycling, hiking, flight simulators, and reading novels, biographies and history. I'm a blue belt in Brazilian Jiu-Jitsu and I enjoy riding motorbikes - here's me and my friends traveling across Vietnam.

Product Engineer

Hello, welcome to this part of my journey on earth as a Human Being :tm: I've been dabbling with computers since 1996 and have amassed an incredible amount of chrome tabs that I will never close. I work on the feature success team building features, hopefully successfully.

In your professional or personal life, if you have:

• Pushed/Pulled a package from GitHub
• Accepted a dependabot PR
• Run workflows on GitHub Actions
• Connected to a PlanetScale Database.

You’ve used code that I’ve written and services I’ve built.

Dylan Martin

In a sentence:

I'm a software engineer, writer, polyglot programmer, career mentor, math enthusiast, ski bum, rock climber, amateur powerlifter, plant dad, jazz pianist, language nerd, and data science dilettante.

In a few more sentences:

I've always been fascinated by the intersection of creativity and constraints. I love understanding how systems work and exploring the grey areas between well-defined spaces.

In college I studied physics and music – the physics helped me understand how the world worked, and then music helped color it in. I don't do a lot of physics anymore, but I still play music regularly.

Professionally, I'm a startup veteran who loves building the foundational systems upon which successful companies are built. I've worked at 3 unicorn startups so far across a variety of industries – health tech, robotics, fintech, and product analytics – and I'm eager to build another!

Here’s what the team is up to.

Latest update

Relative deltas and credible intervals added to a/b tests.

Juraj would like everyone to know that we've now added relative deltas and credible intervals to our A/B testing tool. In order for you to understand how cool that is though, some explanation may be needed...

A relative delta is the percentage change in conversion rate between the control and test variants. So, a bigger delta means a bigger impact for an experiment.

The credible interval is...complicated. Basically, an experiment measures a certain value (like a conversion rate) and the true value isn't actually the result that's displayed - that's just an approximation because an experiment only measures a small sample of the population. The credible interval gives you a better look at the true data by showing a likely range for the results, as well as a probability percentage that reflects certainty.

Relative deltas are pretty obviously useful for a lot of situations where you want to understand the broad improvement, but credible internal is a more advanced metric which is useful for getting into the nitty-gritty of statistical significance.

Finally, we've also made it easier to ship the winning variant when your experiment reaches a significant result, via a shiny new 'Make decision' modal, which you can see above. Snazzy!

• No-code experiments / Visual editor

A visual editor for experiments would allow users to test changes to their website / app without having to touch the code.

Project updates

No updates yet. Engineers are currently hard at work, so check back soon!

See more questions (or ask your own!) in our community forums.

• Question / Topic Replies Last active

Pairs with...

PostHog products are natively designed to be interoperable using Product OS.

Run analysis based on the value of a test, or build a cohort of users from a test variant

Watch recordings of users in a variant to discover nuances in why they did or didn’t complete the goal

Make changes to the feature flag the experiment uses - including JSON payload for each variant

This is the call to action.

If nothing else has sold you on posthog, hopefully these classic marketing tactics will..

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