multcompare function: p-values are NaN if bonferroni method is used

Question

Hannah_Mad 2022-1-10

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回答： Pratyush 2023-12-15

Dear Community,

I have datasets from four groups with sizes of n = 39, n = 15, n = 63, n = 10 and compare these via kruskalwallis yielding p = 0.012. I then attempt post hoc tests (bonferroni method) using the multcompare function as can be seen below. The respective column featuring the p-values displays NaN only. Why is that? When I do not use bonferroni method, but the default option (tukey-kramer) does not result in this problem.

Thank you in advance,

Hannah

z_data = [caps_z; atax_z; dys_z; all_z];
z_group = [ones(size(caps_z)); 2 * ones(size(atax_z));3 * ones(size(dys_z));4 * ones(size(all_z))];
[zP,zANOVATAB,zSTATS] = kruskalwallis(z_data, z_group);
z_posthoc = multcompare(zSTATS,'CType','bonferroni');
p_vals_z = find(z_posthoc(:,6)<0.05);

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Answer 1

Pratyush 2023-12-15

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Hi Hannah,

I understand that you are getting 'NaN' values in the p-value column of the 'multcompare' output when using the 'bonferroni' method, which could be due to:

Small sample sizes: With small sample sizes, the assumptions of the statistical test might not be fully met, which could lead to issues with the calculation of the p-values. However, this is less likely to be the problem since the Tukey-Kramer method works fine.
Zero variance or identical groups: If one or more groups have zero variance (i.e., all values in a group are identical), the Bonferroni method may fail to compute the p-values properly.

You should probably check the group sizes and variances to ensure there are no groups with zero variance.