Very nicely done Chris, but the basic problem with Paul’s analysis is not the mathematics (this can be fixed as you have shown), but the underlying data. Any data set you could get to measure bias in the start-up world is too small and messy to tell you anything useful. No matter how sophisticated your analysis, if the data is garbage then all you will end up with is garbage.
This does even consider the problem of data dredging which First Round Capital engaged in.
Maybe First Round's data set is too small or messy to get meaningful results, but the entire startup world has plenty of data to potentially pull some meaningful conclusions about bias.
I wonder if all YC companies would be enough data points to learn something useful. Or maybe grab a large swath of VC-funded startups including First Round's investments and many other top firms.
Most of the raw stats in Chris's post was above my head, but I'd love to see this applied to a larger data set of fundings.
The basic problem is getting hold of good data. Most VC rightly consider their data in this area very valuable and they are not going to part with it easily. Not even Paul delved into YC’s data.
More fundamentally even if you could get enough data, the data is just too messy to analyse and draw any valid conclusions.
Yeah, a minima-ish statistic (even one less sensitive to noise) is probably not going to work for VC. Noise isn't the only issue - in VC the minima will always be zero unless a VC only picks winners.
A test in this general direction (but which handles noise) is much better suited for answering questions like "are colleges biased against Asians". In that case you have a pretty clear output (college GPA) which very rarely reaches zero.
This does even consider the problem of data dredging which First Round Capital engaged in.