/AB-Testing

Primary LanguageJupyter Notebook

AB-Testing

An A/B Test can be described as a randomised experiment containing two groups, A & B, that receive different experiences. Within an A/B Test, we look to understand and measure the response of each group - and the information from this helps drive future business decisions.

Application of A/B testing can range from testing different online ad strategies, different email subject lines when contacting customers, or testing the effect of mailing customers a coupon, vs a control group. Companies like Amazon are running these tests in an almost never-ending cycle, testing new website features on randomised groups of customers...all with the aim of finding what works best so they can stay ahead of their competition. Reportedly, Netflix will even test different images for the same movie or show, to different segments of their customer base to see if certain images pull more viewers in.

Hypothesis Testing

A Hypothesis Test is used to assess the plausibility, or likelihood of an assumed viewpoint based on sample data - in other words, a it helps us assess whether a certain view we have about some data is likely to be true or not.

There are many different scenarios we can run Hypothesis Tests on, and they all have slightly different techniques and formulas - however they all have some shared, fundamental steps & logic that underpin how they work.

Chi-Square Test For Independence

The Chi-Square Test For Independence is a type of Hypothesis Test that assumes observed frequencies for categorical variables will match the expected frequencies.

The assumption is the Null Hypothesis, which as discussed above is always the viewpoint that the two groups will be equal. With the Chi-Square Test For Independence we look to calculate a statistic which, based on the specified Acceptance Criteria will mean we either reject or support this initial assumption.

The observed frequencies are the true values that we’ve seen.

The expected frequencies are essentially what we would expect to see based on all of the data.

Note: Another option when comparing "rates" is a test known as the Z-Test For Proportions. While, we could absolutely use this test here, we have chosen the Chi-Square Test For Independence because:

  • The resulting test statistic for both tests will be the same
  • The Chi-Square Test can be represented using 2x2 tables of data - meaning it can be easier to explain to stakeholders
  • The Chi-Square Test can extend out to more than 2 groups - meaning the business can have one consistent approach to measuring signficance