A/B Testing: Statistical Significance

Understand when test results are real patterns versus random noise

4 micro-lessons

Preview · progress not saved — Chapter 1 is free

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The Button Color Debate Ends Here

Educational content slides

The argument:

Designer: "Green buttons are more modern and convert better"

Boss: "Blue feels trustworthy, we should use blue"

You: "Let's test it and let data decide"

A/B testing setup:

This is how Google, Netflix, and Amazon make billions in extra revenue—they test everything.

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Test your understanding with this quiz.

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You test two homework app designs for 2 days. Version A: 52 signups, Version B: 48 signups. Your friend says 'Version A wins!' What's the problem?

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Complete this exercise and get AI-powered feedback.

Complete the following task.

Now that you understand testing principles, design a real experiment.

Think about: What variables to control, how long to test, what metrics matter, how to avoid bias.

This reveals: The difficulty of good experimental design and why companies invest heavily in testing infrastructure.

Design an A/B test for something in your life (study method, social media strategy, workout routine, etc.)

What could make your test results unreliable? How would you control for these factors?