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, and improve current standards in data collection.
I hope this introduction will provide a practical primer that allows
researchers to incorporate sequential analyses in their research.
