Abstract
Response time data have a positively skewed distribution. The challenge with this is that a measure of central tendency and dispersion does not adequately describe a skewed distribution. A researcher relying on only response time mean and standard deviation could make incorrect conclusions about response time. The best way to analyze response time data is with a distribution analysis. One reason that response time distribution analyses are atypical is that at least 100 trials are recommended per participant and condition. In the current tutorial, we demonstrate a distribution analysis technique that requires as few as 40 participants with 40 trials per condition. This technique involves geometric quantile averaging (GQA) and the quantile maximum probability estimator (QMPE). Each step of the analysis is detailed with a MATLAB script, flexible MATLAB functions, and experimental response time data. Our goal was to lower the barriers to entry for response time distribution analysis so that more researchers will choose to thoroughly examine response time data.