@jonathan.interviews: What's the danger of peeking early at your A/B test results? Many stakeholders assume if a p-value hits 0.05 on day 3 of a month-long experiment, the result is significant and the test can end early. This intuition is wrong and can destroy your data science. I simulated 1,000 A/B tests where treatment had zero effect. Both groups had a 40% conversion rate over 31 days, recalculating p-values daily. While checking once at day 31 maintains the expected 5% false positive rate, peeking daily and stopping at the first significant result inflates this to 26%. You're five times more likely to declare a winner that doesn't exist. Each peek gives randomness another opportunity to temporarily cross the significance threshold. By day 15, cumulative false positive risk hits 20%. This is why product teams celebrate a winning checkout button redesign on day 5, only to see the effect disappear when they check later. The solution for data scientists and analysts: commit to your sample size upfront and calculate statistical significance once. If business needs demand early stopping, use sequential testing methods like sequential probability ratio tests designed to control error rates under repeated looks. Standard hypothesis testing and p-values aren't built for multiple peeks at your experimental data. #datascience #dataanalytics #abtesting #datascientist #edutokcontest
