![]() Sequential Tests of Statistical Hypotheses. Controlling decision errors with minimal costs: The sequential probability ratio t test. Learn more about our testing and experimentation capabilities. Contact us about working together to evolve your A/B testing program. We’re experts in all the leading platforms. If your testing program uses Optimize, we can help you migrate to another tool. UPDATE: Google’s Optimize A/B testing platform will sunset in September 2023. If efficiency is a priority in your experiment, implementing sequential testing could be the right approach. However, with sequential testing, team members can stay up-to-date on test performance, which is a game-changer allowing you to roll out features to all customers sooner, or pull back if it detracts from the user experience. When running a typical A/B/n test, the experimenter is blind to the data and key statistical measures, such as confidence intervals and p-values. This is great news when faced with limited resources such as budget, time, and/or traffic volume. It is possible to reduce the sample size by 50 percent and end the test earlier than with other testing methods (Schnuerch & Erdfelder, 2020 Wald, 1945). The greatest upside of using sequential testing is efficiency.
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