A bootstrap approach is a superior statistical method for the comparison of non‐normal data with differing variances (2020)
Johnston MG, Faulkner C
Phytologists rely on experimentally perturbing plants and monitoring the responses. Frequentist statistics are used to ascertain the probability that an observed difference between conditions was due to chance (a p value). When data are not normal and have differing variances, we propose data sets are better analysed by a bootstrap method that tests the null hypothesis that means (or medians) are the same between two conditions, instead of the commonly used Mann-Whitney-Wilcoxon test.
We illustrate this with data from the cell-to-cell movement of GFP through plasmodesmata. We found that that with hypothetical distributions similar to cell-to-cell movement data, the Mann-Whitney-Wilcoxon produces a false positive rate of 17% while the bootstrap method maintains a false positive at the set rate of 5% under the same circumstances. Here we present this finding, as well as our rationale, an explanation of the bootstrap method and an R script for easy use. We have further demonstrated its use on published datasets from independent laboratories.
Originally a preprint.