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I have only basic knowledge about bio-statistics. For not normally distributed data, I used Kruskal-Wallis test to investigate the statistical significance between different variables. I performed an exercise with three different intensities (different weights w1, w2, w3), then with one weight at three different speeds (s1, s2, s3). The readings were observed from 3 different points(p1, p2, p3).

I opted for statistical significance among p1, p2, p3 at (w1 and w2 and w3) and at (s1 and s2 and s3). then i opted statistical significance among w1, w2, w3 for p1 and p2 and p3. then i opted statistical significance among s1, s2, s3 for p1 and p2 and p3. So, there are 36 independent Kruskal-Wallis tests.

After that, Mann-Whitney test was performed for pairs (post hoc analysis).

I got comment on the test that, "the piecemeal statistical approach, consisting of a very large number of comparisons made between dependent variables during different conditions, without corrections for multiple testing, renders it probable that “significant” results may well be due to chance."

Can someone please suggest where I am wrong, and how and where to adjust the p value?

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So far, what i found about this particular problem, it should be done by repeated measures of ANOVA, with Bonferroni method for posthoc. The reason is the data is not independent. The p value is needed to be adjusted for multiple tests.

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