My old science professor used to say, “Be careful what you test for”. This is as true in a scientific environment as it is in digital analytics. We’re often asked to look and comment on the results of A/B tests and so many times we find the results are invalid due to the way the test has been set up. Here we offer some simple advice on how you can create fair tests that will mean more meaningful insights in to your optimisation program. Keep things simple Try to keep things as simple as possible. If you have complicated…
