New subject: [Wikimedia-search] On frequency of A/B tests and peeking at the data early

31 Aug 2015

Hi all,

Last week we discussed our approach to A/B testing and we've decided to
have a week (at least) between tests.

A two-week-minimum cadence will give the analysis team enough time to
thoroughly think about the experimental design of each test, as well as
give the engineers enough time to implement it. Which is great because some
of the changes we are planning to test are not trivial and we don't want to
rush a test out and realize halfway through that we should have been
tracking something we're not.

We are also going to move away from doing initial analyses (analysis of the
data from the morning of a launch) for practical and scientific reasons.
Practical in the sense that we've been putting time and effort into getting
preliminary results that are not representative of final results whatsoever
while putting other work on the backburner. Scientific in the sense that
peeking at the data mid-experiment is bad science:

*Repeated significance testing always increases the rate of false
positives, that is, you’ll think many insignificant results are significant
(but not the other way around). The problem will be present if you ever
find yourself “peeking” at the data and stopping an experiment that seems
to be giving a significant result. The more you peek, the more your
significance levels will be off. For example, if you peek at an ongoing
experiment ten times, then what you think is 1% significance is actually
just 5% significance.* – Evan Miller, How Not To Run An A/B Test
<http://www.evanmiller.org/how-not-to-run-an-ab-test.html>

In science, it's a problem called multiple comparisons. The more tests you
perform, the more likely you are to see something where there is nothing.
Going forward, we are going to wait until we have collected all the data
before analyzing it.

Cheers,
Mikhail, Junior Swifty
Discovery // The Swifties