Rank sum tests
Another class of non-parametric hypothesis tests are called rank sum tests. Ranks are the positions of numeric values from smallest to largest. Think of them as positions in running events: whoever has the fastest (smallest) time is rank 1, second fastest is rank 2, and so on.
By calculating on the ranks of data instead of the actual values, you can avoid making assumptions about the distribution of the test statistic. It's most robust in the same way that a median is more robust than a mean.
Two commonly used rank-based tests are the Wilcoxon-Mann-Whitney test, which is like a non-parametric t-test, and the Kruskal-Wallis test, which is like a non-parametric ANOVA.
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Hypothesis Testing in R
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# Run a Wilcoxon-Mann-Whitney test on weight_kilograms vs. late
test_results <- ___
# See the result
test_results