The Lady Tasting Tea by David Salsburg

This is a super niche book for statistics nerds, all the more so because it’s not even technical. Rather, it’s about the people behind all the theorems and proofs – the Pythagorases of 20th century statistics.

**1) Karl Pearson’s key contribution is the idea that math doesn’t have to be deterministic. Instead of one true “thing,” there exists a distribution defined by parameters.**

AP Stats is lowkey the most important class of the 21st century. That said, AP Stats and college stats are not at all the same thing.

**2) Gosset, publishing under the pen name of Student, worked at Guinness.**

Good quality beer calls for good statistical distributions.

**3) Ronald Fisher, who would surpass Pearson in his contributions to the field of statistics, suffered from visual impairment, which enabled him to develop strong geometric reasoning.**

One day, I hope we have VR education. I’d be able to ask any question I want whenever I want and step into a 3D interactive explanation. Knowledge sharing is just too inefficient.

**4) Fisher was a proponent of eugenics and later on would dismiss claims that smoking caused cancer.**

The author painted an overall positive portrait of Fisher, but I’m sure he would not have been fun to work with.

**5) LD-50 is defined as the dose required to kill 50% of the population.**

LD-50 served as the parameter in Bliss’s probit model.

**6) Fisher did not believe that a failure to find significance meant the hypothesis was true.**

The meat of this book was the exploration of competing interpretations of the significance test between Fisher and Neyman-Pearson (Karl Pearson’s son). Fisher believed that an insignificant p-value simply meant we had to conduct another test.

**7) Neyman-Pearson takes the frequentist definition and says that the significance test must pit a null hypothesis against an alternative hypothesis.**

This is the widely accepted interpretation of significance testing now. It’s interesting to consider how it’s not necessarily the “correct” interpretation. With all the p-hacking going on, it’s clear that there are problems with how practitioners take the methodology for granted.

**8) In the Serene Republic of Venice, the head of state doge was elected via a randomly selected set of lectors.**

This story is hard to believe, but it’s good enough for me that the Doge of Venice is real.

**9) Case control, prospective cohort, and retrospective cohort are three types of cohort study.**

Observational studies are hard. Being able to run an experiment is a luxury.

**10) “‘It seems to me that one of statistician’s jobs is to look at figures, to query why they look like they do…. I am being very simpleminded tonight, but I think it is our job to suggest that figures are interesting – and, if the person to whom we say this looks bored, then we have either put it across badly or the figures are not interesting. I suggest that my statistics in the Home Office are not boring.'” – Stella Cuncliffe**

I wholeheartedly agree. One picture is worth a thousand words. A table or chart should be worth at least that.

As a behind-the-scenes addendum to a supposedly dry subject, this book was very easy to read and digestible. I don’t think I’ll remember many details of who discovered what and who didn’t like whom, but the core idea that statistics is a relatively nascent and ever evolving field definitely resonated. Fast forwarding to the present, machine learning and large-scale online experimentation are very much the next step in the statistics evolution.