## A/B testing - confidence interval for the difference in proportions using R

In a previous post we looked at how Pearson's chi-squared test (or Fisher's exact test) can be used to test whether the 'success' proportions are equal under two conditions. In biostatistics this setting arises (for example) when patients are randomized to receive one or other of two treatments, and for each patient we observe either a 'success' (of course this could be a bad outcome, such as death) or 'failure'. In web design people may have data where web site visitors are sent to one of two versions of a page at random, and for each visit a success is defined as some outcome such as a purchase of a product. In both cases, we may be interested in testing the hypothesis that the true proportion of successes in the population are equal, and this is what we looked at in an earlier post. Note that the randomization described in these two examples is not necessary for the statistical procedures described in this post, but of course randomization affects our interpretation of the differences between the groups.

Read moreA/B testing - confidence interval for the difference in proportions using R

## A/B testing and Pearson's chi-squared test of independence

A good friend of mine asked me recently about how to do A/B testing. As he explained, A/B testing refers to the process in which when someone visits a website, the site sends them to one of two (or possibly more) different 'landing' or home pages, and which one they are sent to is chosen at random. The purpose is to determine which page version generates a superior outcome, e.g. which page generates more advertising revenue, or which which page leads a greater proportion of visitors to continue visiting the site.

Read moreA/B testing and Pearson's chi-squared test of independence