When designing experimental studies with human participants, experimenters must decide
how many trials each participant will complete, as well as how many participants to test.
Most discussion of statistical power (the ability of a study design to detect an effect) has
focussed on sample size, and assumed sufficient trials. Here we explore the influence of
both factors on statistical power, represented as a two-dimensional plot on which iso-power
contours can be visualised. We demonstrate the conditions under which the number of trials is particularly important, i.e. when the within-participant variance is large relative to the
between-participants variance. We then derive power contour plots using existing data sets for
eight experimental paradigms and methodologies (including reaction times, sensory thresholds,
fMRI, MEG, and EEG), and provide example code to calculate estimates of the within- and
between-participant variance for each method. In all cases, the within-participant variance
was larger than the between-participants variance, meaning that the number of trials has a
meaningful influence on statistical power in commonly used paradigms. An online tool is provided (https://shiny.york.ac.uk/powercontours/) for generating power contours, from which the
optimal combination of trials and participants can be calculated when designing future studies.