As I've written about before, as a data scientist supporting a product or marketing team with A/B testing, the job is communication -- helping to translate between business requirements and what we can learn from statistics. I (and many, many others) have found that there is a lot of value in having a document, shared …

Recently I wrote a blog post that mentioned “Superiority” as a type of A/B test decision. In this post I want to talk about all five types of A/B test decision that I think are relevant. This is an adaptation and extension of a talk I gave last year at the Quant UX conference (it’s a great event, you should check it …

The "best practice", when evaluating the results of an online controlled experiment (A/B test), is to use classical statistical tests, proceeding with a change if (and only if) the result of the test includes a p value of less than 0.05. But, the American Statistical Association (ASA) said in a prominent 2016 …

A/B testing is a tool for supporting decision-making in business, and so in addition to getting the statistics right, it’s really important to communicate well with the non-statisticians who will have the final say on the go/no-go decision. Most A/B tests in practice are testing ratios, conversion rates of various …

Suppose you’re a data scientist at an e-commerce web site that sells shoes, responsible for supporting A/B tests. Many A/B tests are easy, and there are a number of companies that sell tools that make the easy cases as simple as clicking a few buttons and looking at pretty graphs. But A/B tests can get statistically …