In any A/B test, we use the data we collect from variants A and B to compute some metric for each variant (e.g. the rate at which a button is clicked). Then, we use a statistical method to determine which variant is better.
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Similar ideas to Experimentation Background
In Bayesian A/B testing, we model the metric for each variant as a random variable with some probability distribution.
By accepting variants that offer a small improvement, Bayesian A/B testing asserts that the false positive rate — the proportion of times we accept the treatment when the t...
Decide what to test, create two versions, decide on how long to run the test, collect enough data, analyze.
If you’re running an on-site test, you’ll want to think of all the sales-related pieces of your website, and then figure out which elements to split test:
We can't know what is worth doing if we don't know where we are going.
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