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> identify or avoid methodological flaws like p-hacking,

It is not trivial to identify them. If it was then there would be no replication crisis. Avoiding is easy in theory: you need decide on math methods in advance. Generate random data before you start to gather real data, and write an R program to process this data. Test it, debug, and when you get real data just feed it to this program. Without changing the program. It is harder in practice though.

> understanding correlation vs causation

> I'd love more information on best practices especially for experimental design

I learnt it with experimental psychology. ะก. James Goodwin "Research in Psychology"[1], there are some specific psychological topics covered (you might not be interested in ethics of psychological research), there are not a word about chi squares or other math methods you mentioned (data processing is out of the scope of the book), but there are a lot about different experimental setups, with a lot of examples. IIRC there is discussion of p-hacking too.

I believe this book is a good read to anyone interested in design of experimental and/or correlational research methods in general, not just for psychologists.

[1] https://www.amazon.com/Research-Psychology-Methods-Design-8E...

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