Baysian updating

Des says, “A/B testing helps us gain confidence in the change we’re making.It helps us validate new ideas and guides decision making.

Notice the shapes of the curves, and how they change when you move the sliders.“We didn’t realize it at the time, but when we started A/B testing, we took a very strict approach in the calculations to determine sample size.As a result, we were running tests for an unnecessary length of time and most were deemed inconclusive.If you hang out on Meta Stack Overflow, you may have noticed news from time to time about A/B tests of various features here at Stack Overflow.We use A/B testing to compare a new version to a baseline for a design, a machine learning model, or practically any feature of what we do here at Stack Overflow; these tests are part of our decision-making process.Typical statistical standards for these quantities are 80% for power (i.e., 20% chance of a false negative) and 5% for significance level. That’s a great question with a fair amount of baggage and tradition behind it.If we choose standards that are too strict, perhaps 95% for power and 1% for significance level, all our A/B tests will need to run longer and we will have to invest more time and resources into testing.We need bigger sample sizes to measure small effect sizes, or to achieve low significance levels.If the baseline rate is higher to start with, the sample size needed for a given power goes down.Our second question here is not about statistical standards, but instead is about how big of a difference we expect to see with the proposed change compared to the status quo.Some phrases that people use to talk about this concept are effect size, expected improvement, and improvement threshold.

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