See this answer for some ways to do that.įinally, my experience with Western blots is that reviewers will be most convinced if you show visually distinct differences between samples of the 2 groups that were run in parallel on the very same gel and then blotted and stained together. I suspect, however, that for power analysis you will be better off using the information you have about the variance of Western blot measurements within treatment groups as a guide, calculating the power based on an assumption of within-group normality. There are ways to do power calculations for the Mann-Whitney test, explained for example on this page, based on those principles. To evaluate the number of cases to provide adequate power, you need to have an estimate of the variability of the measurements and the magnitude of the true difference you would like to be able to detect. The question is whether a limited number of samples can provide the statistical power to find that a true difference between two groups provides statistical significance, given the variability among the measurements. Third, you can perform a statistical test with a limited number of samples. That said, the Mann-Whitney can be a very good test of whether 2 sets of unpaired observations differ in that way. The Mann–Whitney U test tests a null hypothesis of that the probability that a randomly drawn observation from one group is larger than a randomly drawn observation from the other is equal to 0.5 against an alternative that this probability is not 0.5. The Mann-Whitney U test does not compare the means of two groups. I am planning on using the Mann-Whitney U test since I do not want to assume that my data is normally distributed. I want to perform a statistical test to compare the means of the two groups to see if there is a significant difference. First, make sure that you are following all the necessary principle for performing quantitative Western blots.
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