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A/B Testing 101
Learn how to run effective AB tests from planning through decision-making and more!
Created by Mel Restori, offered on Udemy
To make sure that we score courses properly, we pay a lot of attention to the reviews students leave on courses and how many students are taking a course in the first place. This course has a total of 925 students which left 284 reviews at an average rating of 4.31, which is average.
We analyze course length to see if courses cover all important aspects of a topic, taking into account how long the course is compared to the category average. This course has a length of 2 hours 54 minutes, which is pretty short. This might not be a bad thing, but we've found that longer courses are often more detailed & comprehensive. The average course length for this entire category is 5 hours 20 minutes.
This course currently has a bestcourses score of 5.2/10, which makes it an average course. Overall, there are probably better courses available for this topic on our platform.
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
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
- None - you're ready to start this course!