Use of Information Priors in A/B Testing

Last time I wrote about the use of prior knowledge in A/B testing there was considerable push back from the analytics community. I think I touched a nerve when I suggested the use of “how confident you were before the test” to interpret the results after the test.  While the use of  such information may sound like gut-feel and arbitrary, we must recognize that we implicitly use considerable information priors in A/B testing. The Bayesian methods I used just made the implicit assumptions explicit.

When you finally get down to test two (or three) versions with  A/B split testing, you have implicitly eliminated many other versions. You should stop and ask why you are not testing every possible combination. The answer is you applied tacit knowledge that you have, either based on your own prior testing or well established best practices and eliminated many versions that required no testing. That is the information prior!

Now let us take this one step further. Of the two versions you selected, make a call on how confident you are that one will perform better than the other. This can be based on prior knowledge about the design elements and user experience or an estimate that is biased. This should not surprise you, after all we all seem to be finding reasons why one performed better than the other after the fact.  In fact the latter scenario has hindsight bias whereas I am simply asking you to state your prior expectation of which version will perform better.

Note that I am not asking you to predict by how much, only how confident you are that there will be real (not statistically significant, but economically significant) difference between the two versions. You should write this down, before you start testing and not after (I prefer to call A/B testing as collecting data). As long as the information is obtained through methods other than this test in question, it is a valid prior. It may not be precise  but it is valid.

What we have is the application of information priors in A/B testing – valid and relevant.

Next up, I will be asking you get rid of the test for statistical significance and look at A/B testing as a mean to reduce uncertainty in decision making.

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