Don't do any of this. It's very outdated advice. And you're going to get it wrong anyway. These threshold adjustment methods were invented before we had access to reasonable computers.

There's a far simpler method that covers every case: permutation tests. https://bookdown.org/ybrandvain/Applied-Biostats/perm1.html

You shuffle the data. Say you want to know if viewing time is affected by color. Literally randomly shuffle viewing time and color. Then, look at that distribution. Is the data that you observed significant?

As long as you shuffle everything related to your experiment and you don't double dip into the data you're going to get things right.

This also has the big advantage that it doesn't overcorrect like traditional methods which apply such strict corrections that eventually it's impossible to get significant results.

This post hasn't even begun scratching the surface on what can go wrong with traditional tests. Just don't use them.

This has nothing to do with speed or rigor. Permutation tests are much simpler to run and faster to analyze. Sadly we keep teaching crappy statistics to our students.

Permutation tests don't account for family-wise error rate effects, so I'm curious why you would say that "it doesn't overcorrect like traditional methods".

I'm also curious why you say those "cover every case", because permutation tests tend to be underpowered, and also tend to be cumbersome when it comes to constructing confidence intervals of statistics, compared to something like the bootstrap.

Don't get me wrong -- I like permutation tests, especially for their versatility, but as one tool out of a bunch of methods.