Amazing AB test result? Check for bugs first, and always set triggering

Amazing AB test result? Check for bugs first, and always set triggering

TL;DR

When an AB test shows an amazing result, assume a bug first: verify tracking, the feature, and the experiment setup before celebrating (Twyman's Law). And always define triggering, evaluate only the users who could actually see the change. Including unaffected visitors dilutes the effect, drags out runtime, and can make a real win look like noise.

Use this when you run into the situation in the title.

When not to use this skill: unrelated tasks. It covers only the procedure above.

Compatibility

The steps above apply to the commands named in them. This skill does not pin a version, so if a flag looks different on your machine, check your installed version's docs first.

Details

Context: Web (dev.to, 5 most common AB testing mistakes): describes two classic traps. Twyman's Law: any figure that looks interesting or different is usually wrong, so an amazing early result usually means a bug in the feature, the tracking, or the experiment design, and you must double-check before presenting it. No triggering: if a feature only affects registered users but you evaluate all visitors including anonymous ones, the dilution kills sensitivity and can make the test impossible to evaluate.

Source: Web (dev.to, 5 most common AB testing mistakes): describes two classic traps. Twyman's Law: any figure that looks interesting or different is usually wrong, so an amazing early result usually means a bug in the feature, the tracking, or the experiment design, and you must double-check before presenting it. No triggering: if a feature only affects registered users but you evaluate all visitors including anonymous ones, the dilution kills sensitivity and can make the test impossible to evaluate.

Verified resolution: When an AB test shows an amazing result, assume a bug first: verify tracking, the feature, and the experiment setup before celebrating (Twyman's Law). And always define triggering, evaluate only the users who could actually see the change. Including unaffected visitors dilutes the effect, drags out runtime, and can make a real win look like noise.

Source URL: https://DEV.to/daelmaak/5-most-common-ab-testing-mistakes-4bk1