A/Btesting, also known as split testing or hypothesis testing, is a powerful tool that lets you optimize business performance by helping you make data-informed decisions.

A/B testing has countless applications. Afew examples:

• Marketers A/B test campaigns to maximize ROI

• Product managers A/B test new features on their website and apps to optimize the user experience

• Data scientists use A/B testing to improve their algorithms

Unlike most other courses, A/B Testing 101 isn't just about the mechanics of A/B testing. It's not only about what numbers to plug in to a calculator and what numbers to read out. Instead, this course goes into the full life cycle of experimentation - from planning through making data-informed decisions.

Specifically, in this course you'll learn how to get the most from your experiments. You'll see:

• How to figure out what to test (develop an learning plan)

• How to plan and execute A/Btests in a way that will let you get the most insights, while reducing the time needed to run those tests

• How to interpret test results, and other information, to make good decisions

• While you won't learn statistical formulas in this course, you will come away with a strong grasp of the intuition and underlying principles behind those formulas so you can effectively run experiments and interpret results

• Whether an idea should be A/B tested, and alternatives to A/Btesting

• How to avoid common pitfalls in A/Btesting

As part of the course material, you will also get these tools to help you implement A/Btesting best practices:

• Experiment planning form

• A/B Testing Calculator Reference

• Sample Experiment Decision Making Flow Chart

I will also provide you links with optional reading material so you can learn about additional concepts related to A/B testing.

Tags:A/B testing, hypothesis testing, split testing, experimentation, statistical significance, t-test, ABtesting