Hello Vlastimil,

As you wrote in the handbook, the P-value is logarithm of probability, that the individual observation belongs to the base population. If this logarithm is lower, than logarithm of the significance level alpha, then the zero hypothesis should be rejected (= the observation did not belong to the base population/observations set). Is this correct, please?

Yes, that sounds right to me. You can strip the logarithm from the interpretation. If $p < \alpha$, the null hypothesis is rejected.

As far if it is correct, then there is not clear, how the rows highlighting in accordance to P-value works. The previous statement splits the interval of possible results into two parts only - the part above significance level logarithm, where is the zero hypothesis valid, and the part under the significance level logarithm, where the zero hypothesis should be rejected. But in row highlighting settings is at the P-value two predefined values, which means the interval of possible values is splitted into three parts, instead of two.

Yes, that is right. The boundary values are taken from the Cadastre (in Germany). Here, 1 % and 5 % are used. For this reason, I have defined these values as default - but you are free to choose your own values.

Then I want also to be clarified the behavior of highlighting. The P-value highlighting is not matching the exact value of logarithm of significance level, which is for 0,1 % (= 0,001) = -6,91, there are some rows highlighted regardless the condition log(p) > log(alpha).

This sounds like a bug and I have to reproduce this behavior by my own creating an example. The pie-charts in the HTML report seem to be correct. However, currently I'm at a measurement campaign and out of office. So, it will take a few days until I can check my source code.

I will come back to this thread as soon as possible.

/Micha