In experiments, researchers must balance between two competing arguments with respect to the sample size. On the one hand, the sample size must be large enough to have sufficient statistical power for accurate statistical inference. On the other hand, each additional observation comes at a cost and, especially when performing medical experiments or working with test animals, the researcher has the ethical obligation to avoid unnecessary oversampling. The field of optimal stopping, or sequential sampling, studies ways in which to do this. Various techniques for sequential sampling are available. This entry, based on the 2019 work of Casper J. Albers, explains some of these techniques.

Sometimes, researchers simply collect and analyze a sample, look at the resulting p value, and collect more data until either significance ...

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