Reconsidering min()?

I've done a little experiment: I run timeit.repeat 1000 times on a really simple statement. After that, I sorted the list and I grouped the results in time slices, counting the number of times a bench is inside a certain time slice. This way I have a sort of probability. Then I plot the result:

https://www.dropbox.com/s/f4naryc055k42cs/2020-07-24-080340_1366x768_scrot.png?dl=0

The result is quite interesting. As you can see, the distribution is not a normal distribution. It seems a multimodal normal distribution. Furthermore, the highest probability is near the minimum value, and not near the average (that is on the second peak).

Mean and stdev are only meaningful if the distribution is a normal distribution. It seems that the way you can have a sort of normal distribution is to consider only the first part of the plot.

Since pyperf returns mean and stdev, does it "cut" the slowest runs?

Start by reproducing the reported timeit.repeat distribution and compare it with the mean and standard deviation that pyperf reports. Read the benchmark-statistics documentation and implementation to determine whether slow runs are excluded; done means the behavior is verified and the issue has a clear decision or documented explanation.