# Supplement on the Kruskal-Wallis test. So what do you do if you don t meet the assumptions of an ANOVA?

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1 Supplement on the Kruskal-Wallis test So what do you do if you don t meet the assumptions of an ANOVA? {There are other ways of dealing with things like unequal variances and non-normal data, but we won t learn them here. Most of these require transformations, and transformations come with a whole set of problems that we don t have time to deal with. See 2.7 [2.7] {2.7} particularly the section on non-linear transformations to get an idea of what kind of transformations we mean. The text doesn t really point out what some of the problems are when using transformations.} As long as the data are still random, we can employ what s called the Kruskal-Wallis test. The only assumption we still need to worry about is randomness. But what are we testing now? - H0: All k population distributions are identical - H1: At least one of these is different (tends to yield larger/smaller observations than the others). - if we assume distributions are similar except in location, we can use means (or medians) instead. Then proceed as usual: - select α - calculate test statistic (let s call it W*) - compare it to a value from KW tables - if W* to W table (but see below), then reject. So, what is W*? It s a bit messy, and is given by: W * = 1 S 2 k 2 i=1 n i N N Yech! But let s not give up yet. It s really not that difficult. Let s figure out what the various parts of this are:

2 - N = total sample size (= n*, using our ANOVA notation) - = sum of the ranks of the i th sample: n i = R X ij j=1 - this says, take the rank of each of the x s and sum these values for each sample (this is very similar to K 1 and K 2 in the Mann- Whitney test, except now we re using ranks instead of number smaller in other sample ) - notice that the very first thing we ll have to do is to rank our observations from smallest to largest (write in the rank next to our each of our observations). - S 2 = an analogue of the variance (notice this is capitalized). It s given as follows: S 2 = 1 N 1 R X ij 2 N 1 2 N all ranks 4 - okay, now you re really worried. But let s stick with it for just a little longer. - this says, Sum up all the square of each of the ranks, then subtract the other quantity (the one involving N). That s not too bad. It s actually very similar to the calculator formula for variance that we didn t have a chance to talk about (but are in my notes). - Now we know how to calculate W* (it ll be much more obvious after an example). But what about W? - This is a bit of a problem. Remember how you needed to have n and n in the Mann-Whitney U-test? - now we have 2, 3, 4... or more samples, so we really can t list the probabilities for everything. - Let s take another quick look at W*. It turns out if there are no ties, we can re-write it as: W * = 12 k 2 3 N 1 N N 1 i=1 n i

3 - (if there are no ties, then use this formula - it s a lot easier) - the stuff in the sum symbol is kind of analogous to what we observed -we re using the actual ranks and getting a kind of sum (squared and adjusted by n i, but still a sum). - the 3(N+1) term is kind of what we would expect for the sums if the ranks were basically equal in each sample. - the term in front of the sum symbol is another type of expected value. {Note: the sum of a bunch of numbers going from 1...n = n(n+1)/2, so you might recognize that both 3(N+1) and the quantity out front are a little similar)}. - so we have an observed quantity (-) an expected quantity, where the observed is also divided by an expected quantity. Sound just a little familiar? - As it turns out W* has (approximately) the Chi-square distribution. - So what do you look up for W? A Chi-square value with k-1 degrees of freedom. - As mentioned, this is an approximation, and exact tables do exist, but they take up a good portion of a book! Minitab seems to do approximation. (More sophisticated software like SAS has an option for getting the exact values of W). - So how does it all work?? An example (let s use the Sheep example, but this time we ll do a KW test instead of an ANOVA) - let s do H0: the diets are all the same. H1: at least one of the diets is different. α =.05 Diet 1 Diet 2 Diet 3 value rank rank 2 value rank rank 2 value rank rank Sum ( ) n i 3 5 4

4 - Notice that N = n* = 12; also notice that the highest rank is 12, so everything s fine so far (if one or more values are tied, you need to use the average rank). - Now we calculate S 2 : S 2 = = Now we merely plug all this into W* to get: W * = = Let s get W table, using our Chi-square table and 2 d.f.: W table = And since W* < W table, we fail to reject and conclude we have no evidence to show the diets are different. (Incidentally, the same result we got with ANOVA) - Okay, let s summarize: - When do you use KW? When you don t meet the assumptions of ANOVA: - As usual, with a larger sample size, ANOVA will start to do better, and you don t need to worry about the normal assumption as much. - What about equal variances? Well, use a little common sense. Don t just assume they re unequal unless you only have two categories (then use a t- test or Mann-Whitney!). This is kind of the opposite to what was said before. If you re worried about them being seriously unequal, use a KW test, but be aware that to use the KW test for means (or medians), it too, assumes equal variances. - How about power? Pretty good; even when the data are normal it doesn t do too badly. - Why not use it all the time? - ANOVA is much more flexible. As mentioned, the number of designs available for ANOVA is almost endless. - and even though the power isn t bad, it isn t the best test to use if data are normal.

5 - Multiple comparisons are available for the KW test, but we just don t have time to dig into that as well. - We didn t do too much with the theory here. The basic idea is very similar to that of the Mann-Whitney U test, the only odd thing is that we can get away with using Chi-square tables instead of exact tables. - The easy way to do this (as usual) is to use a computer package; for example, here's the printout from R: R: Kruskal-Wallis rank sum test data: gain by diet Kruskal-Wallis chi-squared = , df = 2, p-value = (R calls the test statistics Kruskal-Wallis chi-squared ) - Finally, what about two way or more complicated designs? - The Kruskal-Wallis test is designed for one-way type analyses. - There are two-way non-parametric tests; if you need to use something like this, you can look up the Quade test of the Friedman test (the Friedman test is related to the sign test). - A non-parametric test is a test that doesn't assume a particular distribution for the date. The sign test, the Mann-Whitney test and the Kruskal-Wallis test are three examples that we've talked about. We'll discuss at least one more.

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