Free A B testing statistics ebook course with free certification. Learn metrics, confidence intervals, p values, sample size, and decision making for product marketing.
Course content
A/B Testing Essentials: Controlled Experiments for Product and Marketing Decisions
2Metrics for A/B Testing: Choosing Outcomes That Match Product and Marketing Goals
3Randomization and Experiment Integrity in A/B Testing
4Statistical Building Blocks for A/B Testing: Distributions, Variability, and Estimators
5Interpreting Lift in A/B Testing: Absolute vs Relative Changes and Business Impact
6Confidence Intervals for A/B Testing: Quantifying Uncertainty in Differences
7Hypothesis Tests in A/B Testing: p-values, Error Rates, and Decision Rules
8Sample Size Intuition for A/B Testing: Power, Detectable Effects, and Runtime Expectations
9Variance Reduction Basics for A/B Testing: Getting Clearer Results Faster
10Common Traps in A/B Testing: Peeking, Optional Stopping, and Repeated Looks
11Novelty Effects, Seasonality, and Interference: When Results Don’t Generalize
12Metric Misuse and Multiple Comparisons in A/B Testing: Avoiding False Discoveries
13From Results to Decisions: Communicating A/B Test Findings with Statistical Confidence
Course Description
A B Testing Essentials Statistics for Product and Marketing is a practical ebook course that helps you run controlled experiments with confidence. If you make decisions about product changes, landing pages, pricing, onboarding, email, or ads, this course connects basic studies in statistics to the real work of choosing winning ideas and avoiding costly false positives.
You will build a strong foundation in A B testing by learning how to define success metrics that match product and marketing goals and how randomization protects experiment integrity. Along the way you will develop the statistical building blocks behind modern experimentation, including distributions, variability, estimators, and the meaning of lift in both absolute and relative terms so you can translate results into business impact.
The course shows how to quantify uncertainty with confidence intervals and how to use hypothesis tests responsibly, including p values, error rates, and clear decision rules. You will gain sample size intuition through power, detectable effects, and runtime expectations, and you will see how variance reduction can produce clearer results faster without compromising validity.
Real world experimentation is rarely perfect, so you will learn to recognize common traps such as peeking, optional stopping, and repeated looks, plus issues like novelty effects, seasonality, and interference that can prevent results from generalizing. You will also learn how to avoid metric misuse and multiple comparisons that can lead to false discoveries, and how to communicate findings in a way that stakeholders can trust.
Start this free statistics ebook course today and turn A B testing results into confident product and marketing decisions.
This free course includes:
13 content pages
Digital certificate of course completion (Free)
Exercises to train your knowledge
100% free, from content to certificate
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