Phytologists have a primary interest in understanding plant growth, development and environmental responses. Experimentally, we rely on probing these by perturbing a system and monitoring changes, whether it be growth rate with temperature or gene expression in response to stress. To assess the significance of data from such experiments, frequentist statistics are used to ascertain the probability that a difference in a test statistic between conditions was due to chance (a pvalue). When data are not normal, the adage is to use a non-parametric test for this analysis: the most common being the Mann-Whitney-Wilcoxon (MWW) test. Here, we explore conditions in which the MWW test is unsuitable and propose the use of a bootstrap approach instead.