Become great at statistics for A/B testing
Avoid costly testing mistakes stemming from misuse and misunderstanding of statistic
If you’re not fluent in A/B testing statistics, you won’t be able to tell whether your tests suck.
By Georgi Georgiev,
Owner @ Web Focus
Course length: 3h 30min
Some of the companies that train their teams at CXL:
A lot of your “winning” tests are probably not winners at all. Learn to call bullshit when needed, and be the person who advocates proper scientific approach in your team.
In 10 sessions, you’ll learn
- How to run A/B tests with a sound statistical design in a variety of scenarios – multiple test variants, multiple outcomes, non-binomial data, and others.
- How to align the statistical design with the questions at hand to get business insights while avoiding common mistakes.
- The logic behind statistical hypothesis testing and concepts like statistical significance, confidence intervals, statistical power, and others.
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Understand the complexities involved in planning and evaluating A/B tests
Avoid costly testing mistakes stemming from misuse and misunderstanding of statistics, and improve the ROI of all your A/B testing efforts, with Georgi Georgiev’s guidance.
This course is right for you if…
- You can’t define statistical significance correctly without looking it up on Google.
- Your A/B tests produce a lot of “winners,” but your clients aren’t seeing improvements.
- You’re planning and analyzing A/B tests, but you don’t understand the statistical underpinnings of the testing process.
- You’re not confident in the outcomes of your tests and are unsure how much trust to put in them
- You have an in-house statistical tool you want to improve, or you use a third-party A/B testing software you want to understand better
This course is probably not for you if…
- You are just starting with CRO and have little to no practical experience with A/B testing.
- You don’t employ A/B tests as a primary method to evaluate CRO work.
- You are a professional statistician or experimental design specialist.
Skills you should have before taking this course:
- Some experience in conversion rate optimization.
- Basic understanding of how A/B testing works.
- Some experience with an A/B testing software.
Georgi is an expert internet marketer and statistician working passionately in the areas of SEO, SEM and Web Analytics since 2004. He is the founder of Analytics-Toolkit.com and owner of an online marketing agency & consulting company: Web Focus LLC and also a Google Certified Trainer in AdWords & Analytics.
His special interest lies in data-driven approaches to testing and optimization in e-commerce and internet advertising and Georgi is also the author of three papers, multiple articles on A/B testing for conversion rate optimization, as well as the book “Statistical Methods in Online A/B Testing”.
In just 10 sessions, you’ll be able to
- Plan A/B tests by taking into account relevant error probabilities
- Correctly interpret A/B testing statistics like p-values and confidence intervals.
- Navigate the complexities of MVT, segmentation, multiple KPIs, and concurrent tests.
- Efficiently communicate A/B test statistics.
Your full course curriculum
Statistics for A/B testing
- Understand the difficulties involved in making sense of data in a noisy world
- Learn how experiments help us deal with some of those difficulties
- Establish the role of statistics in making business decisions
- Translate business questions into statistical hypotheses
- Understand the need for the p-value and what it means
- Learn how to calculate the p-value for absolute difference in proportions
- Introduce confidence intervals as an alternative presentation of the discrepancy between observations and a model
- Understand type II errors, the concept of statistical power of a test, and how to account for them in planning an A/B test
- Learn how to determine the required sample size for a simple A/B test
- Understand the relationship between power, significance threshold, minimum effect of interest, and sample size
- Be able to plan a fixed-sample A/B test so it achieves a target power at a specified minimum effect of interest
- Understand what challenges are posed by A/B/n designs & the different tools for addressing them
- Understand the implications of increasing the number of variants tested
- Explore the trade-off between testing many variants and testing quickly
- Explore the complications introduced by running concurrent tests
- Understand why commonly-proposed approaches do not get the job done
- Learn how and if to engage in concurrent testing
- Understand the difference between primary and secondary outcomes
- Learn how to recognize situations which require FWER control and which do not
- Design tests with multiple primary and secondary outcomes
- Understand the three types of metrics in A/B testing
- Cut through common misconceptions about statistics based on non-binomial data
- Calculate statistical estimates for non-binomial data
- Explore the difference between absolute and relative or percentage difference
- Define a proper statistical model for percent change
- Calculate statistical estimates for working with percent change outcomes
- Recognize when a one-sided test is appropriate
- Understand the need for correspondence between substantive(business) and statistical hypothesis
- Learn how to translate different business questions to appropriate statistical hypotheses
- Learn how to prepare stakeholders for using statistical insights
- Explore best practices for presenting statistical insights through graphs, stories, tables, etc.
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