Name: The Art of A/B Testing
Rating: 5 (1 reviews)

The Art of A/B Testing

This course is a comprehensive and practical online program designed to equip individuals with the knowledge and skills necessary to conduct effective A/B tests. In this course, participants will learn the fundamental principles of A/B testing, including experimental design, sample size determination, hypothesis testing, and statistical analysis.Show more

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Intermediate

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31 chapters

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Outcomes

Learning outcomes

Modules

Testimonials

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Codefinity advantages

FAQs

What you will learn

Discover the science behind A/B testing, mastering techniques to make informed decisions based on statistical evidence.
Gain the ability to design robust experiments, ensuring the validity and reliability of your tests.
Develop skills to accurately calculate sample sizes and avoid common pitfalls in hypothesis testing.
Learn to interpret test results with precision, transforming data into actionable business insights.

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There are 5 modules in this course

Welcome to the A/B testing course. In this section, we will tell you what controlled experiments are, where they are used, and why they are used. We will look at a specific example of a controlled experiment and talk about A/A testing. The Python programming language will help us with this. Well, let's get started!

Before conducting a controlled experiment, we need to determine the characteristics of the sample. The type of tests that we will conduct a little later will depend on these characteristics. You will learn to build distribution histograms, box plots, and check the normality of the distribution. Let's get started right now!

The last thing to do before conducting a controlled experiment is to check the sampling variances. Some types of graphs and Levene's test will help us with this. Let's talk about it in more detail in this section!

So we got to A/B testing. Soon you will make your first parametric tests, or as they are also called - T-tests. We'll use all the knowledge we've learned in the previous sections: metric definition, normality testing, variance testing, and plotting. All this will help us get statistically significant results. Well, to work!

It turns out that sometimes the samples do not have a normal distribution. This means that we cannot perform parametric testing. What to do in such a situation? Non-parametric testing. We will talk about him in the last section.

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Haitam Kadri

Reviewed on Mar 3, 2024

amazing and practical course

Recommended if you're interested in learning Python

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