Improving Your Statistical Questions

Heads up, Daniel Lakens launched his new course 'Improving Your Statistical Questions' on Coursera yesterday.

Don't be sleeping on Daniel Lakens. The new course is freely available (here)[https://www.coursera.org/learn/improving-statistical-questions]. Also check out his older course ('Improving Your Statistical Inferences')[https://www.coursera.org/learn/statistical-inferences?], can personally recommend that one. Bore your friends with your flawless interpretation of p-values and join the cool kids on (open) science twitter.

His blog, The 20% Statistician, has more specific topics such as (why you shouldn't calculate post hoc power)[http://daniellakens.blogspot.com/2014/12/observed-power-and-what-to-do-if-your.html], (why you should use Welch's instead of Student's t-test)[http://daniellakens.blogspot.com/2015/01/always-use-welchs-t-test-instead-of.html], (how to think straight about p-values)[http://daniellakens.blogspot.com/2019/09/improving-education-about-p-values.html] and other things you were always afraid to ask about stats.

Fuck yeah:

These should be required for everyone (they're also easy):
Improving Your Statistical Inferences (Coursera)
Improving Your Statistical Questions (Coursera)

Then, you can get this book or download the PDF for free. There are also free videos that go with each chapter. This is like the bible of basic stats and will PROPERLY teach you general linear models (GLMs) and how to do them in R. You may have learned correlations, t-tests, and ANOVAs as if they were all different things: they're not. They're all GLMs and they're all fundamentally correlations.
That book has more advanced stuff after, but you don't necessarily need to learn it.

All that and you'll set for undergrad. If you do grad school, you'll learn that (just like any other time you learn math-related stuff), nobody uses that stuff you learned. Fret not, though: learning GLMs is still the foundation! The next step is to learn multilevel-modelling, which is a more advanced form of GLM that takes account of "nested" data. The classic example is students nested in classes nested in schools nested in school-districts: the fact that a bunch of data-points (students) all share a common classroom is ignored in basic GLMs but multilevel-modelling accounts for that. It's easy once you know how to do GLMs and it's just a different line of R code.

Also... learn R. You might start out uncomfortable with coding, but that's life: you gotta push through ignorance to learn. There are plenty of free introductory courses to learn R and this is one skill that will translate even if you pursue other things.

EDIT: If you bounce off the first book, check out Andy Field's "Discovering Statistics". It's the other main "bible" of stats praised by many. I've not used it since I went with the one I linked above (ISL).

While i havent read it myself yet, this e-book seems promising. I also loved Daniel Lakens' course on coursera. I belive he has another newer one now too on the same platform.

You don't need to wait, necessarily. You can start with these two free online courses:
https://www.coursera.org/learn/statistical-inferences?
https://www.coursera.org/learn/improving-statistical-questions

My other thoughts are here. You'll get taught stats, but you'd do well to learn R in the meantime. There are free coursera courses and the Johns Hopkins Data Science one is good for the basics. That's what most students struggle with anyway.

Ultimately, the stats you learn are going to be extremely basic unless you take it upon yourself to learn more than the basic multilevel modelling everyone uses for regression. TBH you'll be way ahead if you understand even basic stats. Most psych professors cannot even correctly define what a p-value is so the competition isn't very stiff on that front.