Research on robust statistics during the past half century provides concrete evidence that classical hypothesis tests that rely on the sample mean and variance are problematic. Even seemingly minor departures from normality are now known to create major problems in terms of increased error rates and decreased power. Fortunately, numerous robust estimation techniques have been developed that circumvent the need for strict assumptions of normality and equal variances, leading to increased power and accuracy when testing hypotheses. Two robust methods that have been shown to have practical value across a wide range of applied situations are the trimmed mean and percentile bootstrap test. To facilitate the uptake of robust methods into the behavioural sciences, especially when dealing with trial-based data such as EEG, we introduce STATSLAB: An open-source EEG toolbox for computing single-subject effects using robust statistics. With the STATSLAB toolbox users can apply the percentile bootstrap test, with trimmed means, to a variety of neural signals including voltages, global field amplitude, and spectral features for both scalp channels and independent components. The toolbox offers a range of analytical strategies and is packaged with a fully functional graphical user interface that includes documentation.