Most A/B tests are lying to you, and the fault isn't in the experiments, it's in the statistics. That's the uncomfortable truth at the heart of the recent piece on Towards Data Science, and it's one every data-driven team needs to sit with. Four specific statistical mistakes invalidate the majority of A/B tests: peeking at results before the sample size is met, ignoring multiple comparison corrections, using p-values without understanding their limitations, and misapplying frequentist methods when a Bayesian approach would be more appropriate. These aren't edge cases. They are the default habits of teams under pressure to ship decisions quickly.
For anyone who relies on A/B tests to guide product changes, marketing campaigns, or feature rollouts, this is a direct challenge to your confidence. If your team has ever celebrated a 95% confidence result after checking the test on day three, or run five variants against a control without adjusting for the increased chance of false positives, you have likely acted on noise. The pre-test checklist is the practical fix: define your minimum detectable effect, set your sample size beforehand, and commit to not peeking until that threshold is met. Do that, and you eliminate the most common sin by simple discipline.
The deeper insight, though, is the choice between frequentist and Bayesian frameworks. Frequentist methods dominate the industry because they are taught first and embedded in most A/B testing platforms. But they answer a narrow question, what is the probability of seeing this result if the null hypothesis is true? Bayesian methods answer the question you actually care about: given the data we observed, what is the probability that version B is better than version A? The decision framework gives you a concrete way to switch, using prior information and posterior distributions to make decisions that reflect your actual risk tolerance, not an arbitrary p-value threshold.
Here is what this means for you on Monday: stop treating A/B tests as automated truth machines. Start treating them as structured bets. Use the checklist to design your test before you collect a single data point. Choose a Bayesian framework when your sample size is small or when you have prior data from similar experiments. And if you are the person signing off on a launch decision based on a p-value of 0.04 from a test you peeked at twice, you are not being data-driven, you are being statistically reckless. The tools to fix that are available. Use them.