I am having trouble figuring out the correction factor to choose for a Bonferroni correction. Let me explain.

I have two datasets, control ($x$) and data from left ($y_1$, affected limb) and right limb ($y_2$, non-affected limb) of a pathology group. As $y_1$ and $y_2$ were from the same subject (paired data), I have performed a Wilcoxon signed-rank test to compare if these data were statistically different.

However, when I compared the data between $x$ and $y_1$ or, between $x$ and $y_2$, I used a Wilcoxon Rank-Sum Test.

As there are multiple tests, should I use correction factor of 3 ($p<=\frac{0.05}{3}$)?

Or ($p<=\frac{0.05}{2}$) for group ($x$ and $y_1$) and ($x$ and $y_2$) only and no correction for group ($y_1$ and $y_2$)?

I would really appreciate your kind reply. Thank you.

  • $\begingroup$ It seems natural to have control data for left and right arms separately, x1 and x2. $\endgroup$
    – ttnphns
    Mar 18, 2014 at 8:41
  • $\begingroup$ Thanks very much for editing, Glen_b. Thanks for your reply, @ttnphns Yes I do have both $x_1$ and $x_2$ data. As these paired data were symmetric, it is a common practice where control's data were averaged and compared with affected and non-affected data from pathological group. Again, the data were measured once only. Data were organised as affected vs non-affected group as these subjects were mixed of left limb total knee arthroplasty and right limb arthroplasty. If I separate, I have to make two groups of pathology subject right? $\endgroup$ Mar 19, 2014 at 0:58

1 Answer 1


In general it shouldn't matter how the p-values were calculated (ie which particular test statistic they came from). A $p < .05$ type decision still has a $5\%$ chance of a false positive, so if you did three hypothesis tests you should correct for three hypothesis tests.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.