Bad experiments are worse than none at all

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Running A/B tests can validate transformative changes but is riddled with potential pitfalls. Common mistakes include having unclear hypotheses, viewing aggregated results without subgroup analysis, including unaffected users, ending tests prematurely, not testing experiments before full rollout, and neglecting counter metrics. Avoid these by ensuring clear hypotheses, breaking down results by relevant user properties, excluding ineligible users, adhering to predetermined test durations, conducting phased rollouts, and monitoring counter metrics.

A/B testing mistakes I learned the hard way

3. Including unaffected users in your experiment

5. Running an experiment without testing it first

<p>I’ll definitely be putting this advice to good use.</p>


<p>Insanely valuable insights! We’ve learned them all the wrong way too. A/B testing sounds simple, but in fact it’s a true mastery to get it done right.</p>
