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Running studies with high statistical power, while effect size estimates in psychology are often inaccurate, leads to a practical challenge when designing an experiment. This challenge can be addressed by performing sequential analyses while the data collection is still in progress. At an interim analysis, data collection can be stopped whenever the results are convincing enough to conclude that an effect is present, more data can be collected, or the study can be terminated whenever it is extremely unlikely that the predicted effect will be observed if data collection would be continued. Such interim analyses can be performed while controlling the Type 1 error rate. Sequential analyses can greatly improve the efficiency with which data are collected. Additional flexibility is provided by adaptive designs where sample sizes are increased on the basis of the observed effect size. The need for pre‐registration, ways to prevent experimenter bias, and a comparison between Bayesian approaches and null‐hypothesis significance testing (NHST) are discussed. Sequential analyses, which are widely used in large‐scale medical trials, provide an efficient way to perform high‐powered informative experiments. I hope this introduction will provide a practical primer that allows researchers to incorporate sequential analyses in their research.
Link to resource: https://doi.org/10.1002/ejsp.2023
Type of resources: Primary Source, Reading, Paper
Education level(s): College / Upper Division (Undergraduates)
Primary user(s): Student
Subject area(s): Math & Statistics