Roland Stevenson shows us how to validate Type I and Type II errors when performing A/B tests in R:

In this post, we seek to develop an intuitive sense of what type I (false-positive) and type II (false-negative) errors represent when comparing metrics in A/B tests, in order to gain an appreciation for “peeking”, one of the major problems plaguing the analysis of A/B test today.

To better understand what “peeking” is, it helps to first understand how to properly run a test. We will focus on the case of testing whether there is a difference between the conversion rates

`cr_a`

and`cr_b`

for groups A and B. We define conversion rate as the total number of conversions in a group divided by the total number of subjects. The basic idea is that we create two experiences, A and B, and give half of the randomly-selected subjects experience A and half B. Then, after some number of users have gone through our test, we measure how many conversions happened in each group. The important question is: how many users do we need to have in groups A and B in order to measure a difference in conversion rates of a particular size?

Read the whole thing. H/T R-Bloggers

Kevin Feasel

2019-08-02

Data Science, R