Hi everyone,

P-value (or fdr), calculated by the t-test, is the common criteria of statistical significance of the measurements between conditions. It is enough to have mean, SD/SEM and N to calculate a p-value between, say, 2 conditions of particular measurement. But there are also cases, when the measurement for the condition is continuous. In my case, I measure the sizes of muscle fibres on the crossections between control and treatment conditions. The common way to interpret the results, and how I also did so far, is to show the distribution of fibres into size bins, say 50-100 um2, 200-300 um2, etc. To claim, for example, that treatment induces increase of proportion of fibres of particular size, we compare the means for particular size-bin and calculate the significance based on the parameters for particular size-bin. Anyway, rather artificial binning of sizes may lead to the incorrect evaluation of the results. For example, the mean and p-value of comparison bin_(100-200 um2) between conditions may be different from those for bin_(100-210 um2), which is not quite biologically meaningful.

To make this more honest, I used the density, which gives the interpolated plot of the continuous frequency-function from the fibre size (see example pic). The red bump is an example of an enrichment of particular fibre-sizes in the treatment group. (This is the summarised plot for couple of replicates per condition) Anyway, now I wonder how to calculate the statistical significance of such enrichment without selecting particular point on the plot (which will be the same story as with binning), but rather using the diapason of values.

Do you have any suggestions how to calculate that? Is there a way to calculate the p-value as a differential equation and then integrate by the size in the particular diapason?

Thanks!

Best,

Michail

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