Glad to be of service :-)

My goal was to give an intuition about multiplicity

In which case you don't need the digression into Sharpe ratios at all. It just distracts from the main point.

the first method that people are taught is to: 1. Take the average daily return over a number of days, and multiply that by 252

Err... If I may offer more advice, don't breezily barge into subjects which are more complicated than they look.

The "average daily return" for people who are taught their first method usually means the arithmetic return (P1/P0 - 1). If so, you do NOT multiply that number by 252 because arithmetic returns are not additive across time. **Log** returns (log(P1/P0)) are, but people who are using log returns are usually already aware of how Sharpe ratios work.

the basic way that people are taught to test for statistical significance

This is testing the significance of the **mean**. I would probably argue that the most common context where people encounter statistical significance is a regression and the statistical significance in question is that of the regression coefficients. And for these, of course, it's a bit more complicated.

Still, both measurements are equally affected by testing multiple hypotheses

I don't understand what this means. If you do multiple tests and pick the best, **any** measurement is affected.

I love your articles! What tool did you use to calculate the linear regression?

I didn't actually do linear regression for the girlfriend matrix, but in general I use the glm function in R. It allows you to quickly generate regression models with any combination of variables by changing a couple of words, and saves the full model for analysis and manipulation.

If you don't need to compare different models (e.g. to pick out which variable are useful and which are just noise), you can even run a regression in Excel (Data -> Data Analysis). If you don't want to install any software at all, Wolfram Alpha got you covered.