Brief Introduction
A/B testing, or split testing, is used by companies like Google, Microsoft, Amazon, Ebay/Paypal, Netflix, and numerous others to decide which changes are worth launching. By using A/B tests to make decisions, you can base your decisions on actual data, rather than relying on intuition or HiPPO's - the highest paid person's opinion! Designing good A/B tests and drawing valid conclusions can be difficult. You can almost never measure exactly what you want to know (such as whether the users are "haCourse Summary
In this course, you will learn how to design and conduct A/B tests, analyze the results, and draw valid conclusions from them. You will also explore how to make decisions based on A/B test results and communicate your findings to stakeholders.Key Learning Points
- Understand the basics of A/B testing and its importance in decision making
- Learn how to design and conduct A/B tests using statistical methods
- Explore how to analyze and draw conclusions from A/B test results
- Communicate your findings effectively to stakeholders
- Make data-driven decisions based on A/B test results
Related Topics for further study
Learning Outcomes
- Design and conduct A/B tests using statistical methods
- Analyze and draw valid conclusions from A/B test results
- Communicate A/B test findings effectively to stakeholders
Prerequisites or good to have knowledge before taking this course
- Basic understanding of statistics
- Familiarity with Excel or Google Sheets
Course Difficulty Level
BeginnerCourse Format
- Self-paced
- Online
Similar Courses
- Intro to Data Analysis
- Marketing Analytics
Related Education Paths
Notable People in This Field
- Ronny Kohavi
- Chris Stucchio
Related Books
Requirements
- This course requires introductory knowledge of descriptive and inferential statistics. If you haven't learned these topics, or need a refresher, they are covered in the Udacity courses Inferential Statistics and Descriptive Statistics . Prior experience with A/B testing is not required, and neither is programming knowledge. See the Technology Requirements for using Udacity.
Knowledge
- Instructor videosLearn by doing exercisesTaught by industry professionals
Outline
- lesson 1 Overview of A/B Testing This lesson will cover what A/B testing is and what it can be used for. How to construct a binomial confidence interval for the results. How to decide whether the change is worth the launch cost. lesson 2 Policy and Ethics for Experiments How to make sure the participants of your experiments are adequately protected. What questions you should be asking regarding the ethicality of experiments. The four main ethics principles to consider when designing experiments. lesson 3 Choosing and Characterizing Metrics Learn techniques for brainstorming metrics. What to do when you can't measure directly. Characteristics to consider when validating metrics. lesson 4 Designing an Experiment How to choose which users will be in your experiment and control group. When to limit your experiment to a subset of your entire user base. Design decisions affect the size of your experiment. lesson 5 Analyzing Results How to analyze the results of your experiments. Run sanity checks to catch problems with the experiment set-up. Check conclusions with multiple methods including a binomial sign test.
Summary of User Reviews
Learn the ins and outs of A/B testing with Udacity's comprehensive course. Students rave about the practical application of the material and the engaging instructors.Key Aspect Users Liked About This Course
Practical application of the materialPros from User Reviews
- Engaging instructors
- Clear explanations of concepts
- Lots of hands-on practice opportunities
- Useful for professionals and beginners alike
- Great for understanding data analysis
Cons from User Reviews
- Can be challenging for those without a background in statistics
- Some technical issues with course materials
- Not enough emphasis on real-world scenarios
- Could benefit from more interactive elements
- Limited opportunities for collaboration with peers
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