Would it? You didnt need to explain the theory behind the KS test. The result is easier to interpret - it could be something like “the $500 tip results in answe

Would it? You didnt need to explain the theory behind the KS test. The result is easier to interpret - it could be something like “the $500 tip results in answers that are 0.95 characters closer to the target, on average”. That seems a lot better than the unitless, weirdly scaled KS values. Bootstrapping works great for any volume of data. Its also nice that mean-difference bootstraps have extremely few distributional assumptions, which is really handy with these unmodelable source data distributions.