Wait, how is any of this relevant if there were only 2 Claude commits? My statistics courses are far behind me, but don't you need at least 30 data points to conclude anything?
Depends on the methods you use. If you're trying to fit curves and so on, yes. The methods I use were designed for very low amounts of data, and are generally okay for that, specifically and especially when you're just trying to show a lack of evidence for some non-null hypothesis.
And again, that's kind of the point. There's exactly zero actual evidence, however you slice it, that "Claude broke rsync" except cherry-picked anecdata, and the whole point of my analysis is to demonstrate the total lack of any such trend/evidence at all, and just how in-distribution/normal these releases are, to show that if people hadn't known Claude was involved in them, they wouldn't have remarked on them.
It wasn't 2 Claude commits. It's 2 releases where the (many) commits were largely co-authored by Claude.
> My statistics courses are far behind me, but don't you need at least 30 data points to conclude anything?
That cuts both ways. If we say that the author here can't claim any conclusion because there are only 2 Claude-authored releases, then we must also say that the people claiming "Claude broke rsync" have no statistical basis to draw that conclusion, either.
It's not uncommon to have small amounts of data come out of experiments. These are appropriate tests for the size of the data. These tests failed to disprove the null hypothesis.
