A/B Testing Clearly Explained
2 min readDec 21, 2022
What is A/B testing, and how would you use R to do it?
A/B testing is a statistical method used to compare two versions of a product or treatment in order to determine which performs better. In R, you can conduct A/B testing using a number of different statistical tests and packages. Here is an example of how you might set up and conduct an A/B test using R:
- Define the null and alternative hypotheses for the test. The null hypothesis is typically that there is no difference between the two versions being tested, while the alternative hypothesis is that there is a difference. For example, if you are testing two different versions of a website, the null hypothesis might be that the two versions have the same conversion rate, while the alternative hypothesis is that one version has a higher conversion rate than the other.
- Divide your sample into two groups: group A and group B. Group A will receive version A of the product or treatment, while group B will receive version B. It is important to ensure that the two groups are similar in terms of relevant characteristics, such as age, gender, and any other factors that might affect the outcome of the test.
- Collect data on the outcome of the test for both groups. This might involve tracking metrics such as conversion rate, user engagement, or any other relevant metrics.