[armanboyaci]

>Being able to apply statistics is like having a secret superpower.

I totally with this sentence. BUT If you ask for my opinion, merely knowing a list of statistical formulas is not very helpful. Most of the time, people don’t remember the underlying assumptions, so there is a fair chance they will use them in inappropriate situations.

I recommend watching these two YouTube videos. The presenters advocate using simulation/bootstrapping/shuffling methods instead of memorizing formulas.

Jake Vanderplas - Statistics for Hackers https://www.youtube.com/watch?v=Iq9DzN6mvYA

John Rauser - Statistics Without the Agonizing Pain https://www.youtube.com/watch?v=5Dnw46eC-0o

[mont_tag]

IIRC, Jake's video inspired the example section in the Python random module docs. It takes about 15 minutes with those examples to learn how to put Jake's ideas into practice. https://docs.python.org/3/library/random.html#examples .

[Terr_]

> The presenters advocate using simulation/bootstrapping/shuffling methods instead of memorizing formulas.

Yeah, I often find it much easier to make a little Python script to do 10,000 monte-carlo trial, as opposed to properly" working things out and then not even being confident-enough in my result anyway.

[asdff]

It makes no sense to memorize the formulas when most any statistical formula you'd actually use has a package or three that can run it in a way that's already probably reasonably benchmarked and not prone to you fat fingering some error rolling your own.

[dapperdrake]

Assumptions are the part that matters.

[wodenokoto]

While I really liked the video by vanderplas, I did return to it after a year or two and paused every time he presented a problem and then tried to solve it using for loops and thinking hard.

I barely succeeded in any of it. So at that point just look up the formula instead of bootstrapping.

I’ll give the second one a shot too.