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A/B Testing

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A/B testing (also called bucket testing or split-testing) is an evidence-based best practice for improving the performance of an application by running a controlled experiment with two variants (A and B). Typically one variant is the current version of the product (the control), and the other contains new changes being tested to see if they improve performance of a metric or set of metrics.

  1. Learn A/B Testing with the Practica AI Coach

    The Practica AI Coach helps you improve in A/B Testing by using your current work challenges as opportunities to improve. The AI Coach will ask you questions, instruct you on concepts and tactics, and give you feedback as you make progress.

  2. Intro to A/B Testing

    A/B testing is a method of comparing two versions of a webpage or app to determine which one performs better. It is a valuable tool for optimizing user experience and increasing conversions.

  3. How To Conduct an A/B Test

    To conduct an A/B test, start by defining a clear hypothesis and selecting a metric to measure success. Create two versions of the page or app, randomly assign visitors to each version, and collect data to analyze the results.
    • Etsy logo
      Guidelines For Ab Testing

      Emily Robinson, Data Scientist, Etsy

      Emily provides 12 guidelines for effective A/B testing, including having one key metric, doing a power calculation to determine test length, not stopping early for significance, paying attention to Confidence intervals over p-values, limiting variants tested, avoiding segment-level differences, and checking for bucketing skew. She emphasizes the importance of involving a data scientist from the start and testing smaller changes incrementally. An interesting point is that revenue is generally a poor choice for the key metric due to its skewed distribution, and proportion metrics are preferable.
    • Airbnb logo
      Experiments at Airbnb

      Jan Overgoor, Data Scientist, Airbnb

      This article uses case studies to explain common pitfalls in A/B tests: hitting "significance" early (solution: calculate sample size for a treatment effect ahead of time), tracking the impact of changes in different contexts (example: different browsers), and making sure your A/B testing system works (using A/A dummy tests).
    • Andreessen Horowitz logo
      Conservation of Intent: The Hidden Reason Why A/B Tests Aren’t as Effective as They Look

      Andrew Chen, General Partner, Andreessen Horowitz

      Andrew helps you figure out where to focus your a/b testing energy: on high-intent buyers toward the bottom of the funnel.
    • Postscript logo
      Inside Growth at Wistia: The Process Behind Our A/B Tests

      Andrew Capland, Head of Growth, Postscript

      This article details the processes, documents, and tools that can be used to establish a culture of experimentation and A/B testing. It describes how to maintain a master ideas log, how to communicate an A/B testing roadmap, and how to log the details of a specific test.

  4. What NOT To Test

    It is important to avoid testing too many variables at once, as this can lead to inconclusive results. Additionally, be cautious when testing elements that could have a negative impact on user experience or violate ethical considerations.
    • Sundial logo
      The Agony and Ecstasy of Building with Data

      Julie Zhuo, Co-Founder, Sundial

      Julie provides 3 pitfalls when working with data: 1. Picking the wrong metric to optimize for 2. Over-pivoting towards what’s measurable 3. Biasing towards the short-term. She also provides 4 pitfalls when performing a/b tests: 1. Spending too long perfecting tests. 2. Shipping successful tests right away. 3. Running too many tests on details that don’t matter. 4. Relying on A/B tests to do anything innovative or large or multi-faceted.

  5. Communicating A/B Test Results

    Communicating A/B test results effectively involves presenting clear and concise data, highlighting key takeaways, and providing actionable recommendations for future improvements.
    • Squarespace logo
      How We Talk About A/B Test Results

      Laura Elsener, Senior Data Scientist, Squarespace

      Laura provides two A/B testing case studies, one successful and one not, and then explains how to communicate the results. The key is to derive learnings from successes and failures.

  6. Technical Considerations of A/B Testing

    Technical considerations of A/B testing include factors such as sample size, statistical significance, and testing duration. It is important to understand these technical aspects to ensure accurate and reliable results from your tests.
    • Netflix logo
      It’s All A/Bout Testing: The Netflix Experimentation Platform

      Steve Urban, Engineering Manager, Netflix

      This article provides a technical overview of how Netflix's homegrown A/B testing platform works, including the system architecture and all workflow steps between the A/B server and client.
    • Uber logo
      Under the Hood of Uber’s Experimentation Platform

      Eva Feng, Data Scientist, Uber

      Eva and her team go behind the scenes of Uber's experimentation platform that powers more than 1,000 tests per month. They explain various components of the system, including its statistics engine, statistics methodology, metric recommendation engine, and its sequential testing capabilities.

  7. A/B Test Case Studies

    A/B test case studies provide real-world examples of how businesses have used A/B testing to improve their websites and apps. These studies can offer valuable insights and inspiration for your own testing strategies.

  8. A/B Testing Cheat Sheet

    Here is a quick reference for the top 5 things you need to know about A/B Testing.

    1. Define Your Goals
      • Identify what you want to achieve through A/B testing. Do you want to increase conversion rates, engagement, or revenue?
      • Set specific and measurable goals. For example, increase the conversion rate by 10% in the next month.
      • Prioritize your goals based on their impact on your business.
    2. Create Hypotheses
      • Develop a hypothesis for each goal. For example, changing the color of the call-to-action button will increase the conversion rate.
      • Ensure your hypotheses are based on data, research, and best practices.
      • Make sure each hypothesis is testable and can be measured.
    3. Create Variations
      • Create variations for each element you want to test. For example, create different versions of the call-to-action button with different colors, sizes, and text.
      • Ensure each variation is different enough to produce meaningful results.
      • Ensure each variation is randomly assigned to a sample group to eliminate bias.
    4. Run the Test
      • Run the test for a specific period of time. Ensure the test has enough traffic to produce statistically significant results.
      • Record the data for each variation and monitor the test regularly.
      • Ensure the test is not affected by external factors such as holidays, promotions, or website changes.
    5. Analyze the Results
      • Analyze the data for each variation and compare them to the original version.
      • Determine the winner based on the primary goal and statistical significance.
      • Ensure the results are valid and reliable before implementing the changes.

  9. Frequently asked questions

    • What are the key steps in conducting a successful A/B test?

      The key steps in conducting a successful A/B test include defining a clear hypothesis, selecting the appropriate metrics and sample size, randomizing the assignment of users to test groups, running the test for a sufficient duration, and analyzing the results to draw conclusions and make data-driven decisions.

    • How can I ensure that my A/B test results are statistically significant?

      To ensure that your A/B test results are statistically significant, calculate the required sample size before starting the test, taking into account the desired effect size, statistical power, and significance level. During the test, monitor the p-value and confidence intervals to determine when the results are statistically significant. It's also important to avoid peeking at the results too early, as this can lead to false conclusions.

    • What are some common pitfalls to avoid when conducting A/B tests?

      Common pitfalls to avoid when conducting A/B tests include testing too many variables at once, which can make it difficult to determine the cause of any observed changes; not running the test for a sufficient duration, which can lead to inconclusive results; and making decisions based on statistically insignificant results, which can result in suboptimal changes to your product or website.

    • How can I minimize the risk of false positives and false negatives in my A/B tests?

      To minimize the risk of false positives and false negatives in your A/B tests, ensure that you have a large enough sample size, run the test for a sufficient duration, and use appropriate statistical methods to analyze the results. Additionally, consider running follow-up tests to validate your findings and account for any potential biases or confounding factors.

    • How can I use A/B testing to optimize my conversion rate?

      To use A/B testing to optimize your conversion rate, start by identifying areas of your website or product that have the most significant impact on conversions, such as landing pages, calls-to-action, or pricing structures. Develop a clear hypothesis for each test, focusing on changes that you believe will improve the user experience and lead to higher conversions. Continuously run A/B tests, analyze the results, and implement the winning variations to iteratively improve your conversion rate over time.