In the SPRT, this analysis outcome is a likelihood ratio in the SBFT, it is a Bayes factor. To decide when to terminate the data collection, researchers monitor an analysis outcome as sample size increases. In sequential hypothesis tests, researchers terminate data collection when sufficient information has been obtained to decide between the competing hypotheses (Wald, 1945). We argue that this interplay should be taken into consideration when planning sequential designs and provide several recommendations for applications. We show that the efficiency of the tests depends on the interplay between the exact specification of the statistical models, the definition of the stopping criteria, and the true population effect size. Therefore, we argue that previous comparisons overemphasized the differences between the SPRT and SBFT. We demonstrate that while the two tests have been presented as distinct methodologies, they share many similarities and can even be regarded as part of the same overarching hypothesis testing framework. Recently, two sequential hypothesis testing methods have been proposed for the use in psychological research: the Sequential Probability Ratio Test (SPRT Schnuerch & Erdfelder, 2020) and the Sequential Bayes Factor Test (SBFT Schönbrodt et al. Sequential hypothesis tests constitute a powerful tool to achieve experimental efficiency. Therefore, it is in the best interest of all stakeholders to use efficient experimental procedures. However, data collection comes at a price: It requires time, monetary resources, and can put participants under considerable strain. ![]() Efficiency in Sequential Testing: Comparing the Sequential Probability Ratio Test and the Sequential Bay AbstractĪnalyzing data from human research participants is at the core of psychological science. This post is an extended synopsis of Stefan, A.
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