A/B Testing 101
Learn how to run effective AB tests from planning through decision-making and more!
Ratings 4.60 / 5.00
What You Will Learn!
- How to successfully run A/B tests - from set up through interpreting results
- The statistical intuition needed to understand A/B testing (no formulas - just intuition)
- How to avoid common pitfalls of A/B testing
- Alternatives to A/B testing
Description
A/B testing, 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. A few 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/B tests 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/B testing
How to avoid common pitfalls in A/B testing
As part of the course material, you will also get these tools to help you implement A/B testing 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, AB testing
Who Should Attend!
- Business professionals (product managers, marketers, etc...) who can use A/B testing to drive improvements to their organizations
- Business leaders who want to understand how A/B testing can be used - and misused - in their organizations
- Data professionals / stats people who want to dive deeper into the practical nuances of A/B testing (note: this course does not teach statistics or formulas)
