A/B Testing Mastery: Statistical Foundations & Strategy

[mitsumi]

Published 5/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 4h 46m | Size: 2.15 GB

Learn hypothesis testing, experiment design, and strategic A/B implementation to optimize user experiences


What you'll learn
Define and formulate testable hypotheses and success metrics for A/B experiments
Calculate sample sizes, statistical power, and interpret confidence intervals to design reliable tests
Differentiate between Type I & II errors and apply Bayesian and frequentist methods for valid analysis
Implement randomization, segmentation, and traffic allocation to eliminate bias and optimize results
Requirements
Basic understanding of statistics, familiarity with spreadsheets or statistical software (Excel, Python, R), and access to an A/B testing platform or simulation environment
Description
This comprehensive A/B Testing Mastery course delivers a complete journey from statistical foundations to strategic implementation. You will start by grounding yourself in core concepts like hypothesis testing, probability distributions, sampling theory, and confidence intervals. Our lessons guide you through calculating sample sizes, power analysis, and interpreting p-values and margins of error with clarity and confidence. Whether you are new to experimentation or seeking to refine your analytical skills, you will acquire the statistical toolkit to validate changes and drive optimized decisions.In the second part of the course, you will dive deep into experiment design. Learn to define precise hypotheses, select meaningful success metrics, and structure randomization schemes that avoid bias. Understand segmentation strategies for meaningful cohort analysis and master the practical steps of traffic allocation across control and variant groups. We also cover monitoring dashboards, stopping rules, and troubleshooting common pitfalls to maintain integrity in every test you run.You will then explore advanced analytical techniques and learn to differentiate between frequentist and Bayesian approaches. Demystify p-value misconceptions, quantify uncertainty with confidence intervals, and contrast posterior probabilities with traditional significance tests. By the end of these lessons, you will skillfully apply statistical power calculations to balance effect size and sample size, reducing Type I and Type II errors in your experiments.Moving beyond analysis, this course emphasizes strategic implementation. Discover how to integrate A/B testing within product roadmaps, prioritize experiments aligned with business goals, and cultivate an experimentation-driven culture. Learn to communicate insights effectively to stakeholders and build cross-functional buy-in for continuous optimization. You will also study multi-armed bandits and sequential testing methods to accelerate decision velocity and personalize experiences in real time.We review popular platforms such as Optimizely, Google Optimize, and AB Tasty, alongside guidance for building custom testing frameworks. You will get hands-on tutorials for setting up tracking instrumentation, creating data pipelines, and validating experiment data integrity. Ethical considerations and data privacy regulations including GDPR and CCPA are woven throughout, ensuring your online tests respect user consent and legal requirements as you scale your experimentation program.By course completion, you will confidently design, execute, and analyze A/B tests that drive measurable growth. Ideal for data analysts, product managers, marketers, and UX designers, this course offers actionable insights to optimize conversion rates, engagement, and revenue. You will also gain access to real world case studies and best practices that you can immediately apply to your projects, accelerating your journey from novice experimenter to A/B testing expert.Enroll today and transform your decision making with data driven experimentation. Start mastering A/B testing foundations and strategic implementation to unlock the full potential of your digital products.
Who this course is for
Product managers, data analysts, marketers, UX designers, and professionals seeking to use A/B testing to drive data informed decisions and optimize digital experiences
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