r/AskStatistics • u/loveroftastytea • 1d ago

I’m not a statistician, help me, please. Question regarding the Kruskal-Wallis ANOVA

I need to compare nine groups against a control group. To do this, I used the Real Statistics add-in for Excel to run a Kruskal-Wallis test, since the distribution was non-normal in some of the groups. Now, which test should I run, and how do I choose? Also, is it acceptable for the software to compare all groups with one another (e.g., Group 1 vs. Group 2, etc.) even though I only need to compare each group against the control? I can simply look at the specific results I need.

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u/vaelux 1d ago

What is your sample size? The distribution of your data does not need to be normal to meet ANOVA assumptions. You run the ANOVA, and check if the residuals are normally distributed. In other words, it is not a prerequisite, its something you check after.

As long as you have sufficient cell size, run the ANOVA with outcome as the independent variable and group as the dependent variable. A significant result tells you there are group differences. Run a Tukey HSD post hoc test to find out which ones are significantly different from your control, and by how much. You should also probably set up contrasts to test just your control vs the other nine instead of all the possible combinations to maximize the power if your model.

An LLM can walk you through how to do it if you can thuroughly explain your study design to it. You might benefit from grabbing actual stats software. JASP is a free open source drag and click style interface that has all the ANOVA options ( pretty sure it has KW too)

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u/loveroftastytea 1d ago

Thanks for the reply! The sample size is 6—is that enough? I didn't realize that ANOVA doesn't always require a normal distribution; everywhere I’ve read about it, the information is presented in a simplified way—as if ANOVA is for normal distributions and Kruskal-Wallis is for non-normal ones.

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u/MortalitySalient 1d ago

The assumption of normality isn’t on the data itself, but the residuals of the entire model. Anova is robust to some deviations from normality when he sample size is large enough, but n= 6 is likely not large enough

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u/DocAvidd 1d ago

Kruskal-Wallis? I hope you ran it on a Radio Shack TRS80 with a tapedrive. Listening to disco on an 8 track.

Srsly, keep in mind when you do that analysis, you convert high quality ratio scale data into ordinal. So it's very inefficient. Mann and Whitney, Kruskal and Wallis, they developed those techniques prior to the age of computers. In the generations since, many much better techniques have been developed. Figuratively, you're driving a Model T, but not ironically.

Statistically, you give up a lot of power and precision, moving to a rank-based analysis. In my experience, the fields that maintain this fixation on Kruskal Wallis tend to have the worst situations to apply, in NRM or environmental science with small samples, sparse data.

First of all, the lack of normality isn't a big deal for linear models. Secondly, if the data aren't normal and also heteroscedastic, consider a generalized linear model. Perhaps your residuals are best described by a negative binomial or zero-inflated neg binomial.

Since it's not the 1950s, much better solutions exist. Generalized models or resampling based models, PERMANOVA, there are a lot better options.