# sequential-testing Published articles for sequential-testing. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Speed with Rigor: testing smarter with group sequential design DevFeed: [Speed with Rigor: testing smarter with group sequential design]() Original publisher: [Read original article]() Author: Gabriela Lewenfus Published: 2025-08-05T17:25:29Z Content type: article Language: en Sources: [OLX]() Topics: [Testing](), [A/B Testing](), [experiments](), [data](), [implementation]() Tags: [a-b-testing](), [ab-testing](), [data](), [efficiency](), [experiment](), [experimentation](), [false-positive](), [implementation](), [pitfalls](), [sequential-testing](), [statistics](), [testing]() ### AI overview The article explains how group sequential testing can replace fixed-horizon A/B tests with monitoring at predefined intervals while controlling error rates. It discusses how this approach addresses peeking, may allow ineffective tests to stop early, and involves practical trade-offs and implementation challenges. ### Source excerpt generated by chatgptIntroduction In the world of A/B testing and experimentation, it's tempting to check results frequently and stop tests earlier if they seem significant (a practice known as peeking). However, this practice dramatically inflates the false positive rate, leading to misleading conclusions. Imagine you're running an A/B test, and after just 10 days, the results look like a clear win. You might be tempted to end the experiment early and move forward with the release of the new feature. After all, why wait another two weeks as originally planned? But here's the catch: the more often you check the results, the higher the chance you'll spot a "win" that isn't real. Frequent peeking increases the risk of false positives -- in other words, drawing the wrong conclusion just by chance. To prevent peeking while ensuring statistical power, a common practice is to set a minimum sample size that must be reached before ending the experiment. However, this fixed horizon approach can extend the experiment's duration and delay decision-making. Group sequential testing (GST) provides a statistical framework to monitor results at predefined intervals while maintaining control over error rates. At OLX, we migrated from fixed horizon experiments to GST to achieve faster insights, lower costs, and greater efficiency, enabling our teams to make smarter, data-driven decisions with speed and confidence. In this post, I'll explain how GST works, how it can accelerate experiments while still preventing the pitfalls of peeking, and how we can use it even to stop ineffective tests early. Finally, I'll discuss real-world trade-offs and implementation challenges. Peeking is a trap Before we dive into GST, let's first understand what "peeking" is and why it's so concerning. Peeking occurs when the experimenter repeatedly checks an experiment's results and finishes it once a statistically significant effect appears. This inflates the false positive rate, leading to unreliable conclu ## Simple Sequential A/B Testing DevFeed: [Simple Sequential A/B Testing]() Original publisher: [Read original article]() Author: Evan Miller Published: 2015-10-13T12:15:00Z Content type: article Language: en Sources: [Evan Miller]() Topics: [A/B Testing](), [Testing]() Tags: [a-b-testing](), [conversion](), [sequential-testing](), [statistical-significance](), [testing]() ### AI overview Evan Miller presents a simple sequential A/B testing procedure that allows experiments to stop early when treatment performance appears superior or to stop without declaring a winner when the combined number of successes reaches the chosen sample size. The method is described as especially effective for low-conversion-rate experiments. ### Source excerpt Cut your A/B sample sizes in half, using this one weird trick: Simple Sequential A/B Testing