Hypothesis testing with the A/B test

A/B-testing is a tool that allows you to test hypotheses with division into different user groups, and helps you compare the results of the hypotheses being tested.

A/B testing is a tool that allows you to test hypotheses on different parameters and helps you compare the results of the hypotheses being tested.

Any project is always in search of ideas for growth, hence the hypotheses arise, but how to test whether this or that idea will work?

This is where we need A/B tests or split tests

Before you start testing, form a list of hypotheses about where there are problematic areas or possible points of growth. Then evaluate them: which of the suggested ideas might yield the most tangible results?

What types of hypotheses can be tested:

– advertising messages – text and images;

– targeted audience settings;

– devices;

– changes to the site – structure, texts, images, colors, etc;

– campaign strategies and types.

Obviously, this is far from an exhaustive list. Almost any parameters can be tested.

How to evaluate test results

You need to assess how much variation there is in test results. Compare between experiments such indicators as: CR, CPL, CTR. If the significance of the results is clear enough, the test can be considered successful. Always look for growth points of the campaign and improve your results, conduct tests and implement non-standard solutions.