How to Get More Statistical Power from Fewer Research Participants

Statistical power analysis often requires an impractically large number of participants for research studies, making many experiments infeasible. A proposed solution involves using a within-subject design where each person experiences multiple conditions and completes several tasks within each one. This method treats the multiple task outcomes as semi-independent data points and uses multilevel regression to control for the dependency, thereby boosting statistical power. The author provides an online Monte Carlo simulation to help researchers estimate the required number of participants and tasks needed to achieve sufficient power with this approach.