Is this graph heteroscedastic and if so is Kruskal Wallis test invalid? I'm new to stats and using GraphPad Prism9 to look at homoscedacticity of a Kruskal Wallis test. Just wondering if this is heteroscedastic because it's cone shaped to me? Also would this mean that the Kruskal Wallis test is void as heteroscadistic means the distribution between each group is not the same?

 A: The plot suggests mild heteroskedasticity (what are the sample sizes?).
However, this potential heteroskedasticity may be of little consequence.
Despite many sources saying otherwise, you do not have to have constant sample variance for the Kruskal-Wallis test to behave as it should.
You assume identical distributions under $H_0$ (in order that there's exchangeability) but you don't need to assume constant spread under $H_1$. Since $H_0$ is probably not exactly true, the appearance of the samples may not be helpful in relation to that; in particular, if spread changes as the mean increases (not necessarily proportionally), everything may still be fine.
Consider, for example, that the Kruskal-Wallis test is invariant to monotonic transformation. If you had constant variance across the whole of $H_1$ on some scale you would not have it after any nonlinear transformation of the variables -- yet the Kruskal-Wallis test would not change on the new scale; it would have the same test statistic. Clearly, then, the spread and the shape might both change as the locations change, without any harm to the Kruskal-Wallis.
Correspondingly, you don't need to be considering pure location-shift alternatives, either (another fairly common but mistaken assertion).
Consequently you might have nothing to worry about at all.
[Edit: one thing that does concern me a little is the seeming coincidence of multiple points; is the variable discrete?]
Where do the values in your plot come from? Are they output from the Kruskal-Wallis procedure in Graphpad Prism? If so, the terms in the plot -- 'residuals' and 'predicted values'  -- seems as if it may itself be based on assuming a location-shift alternative, which as I mentioned needn't apply for the Kruskal-Wallis to be perfectly valid.
Does Graphpad Prism suggest that you must have homoskedasticity in your sample data to use Kruskal-Wallis?
