In section 13, I see they are still teaching the Fisher-Neyman Pearson hybrid (ie the null ritual). For a brief overview see [1]. To start you off: Fisher said the idea of power was nonsense[2], and Neyman-Pearson said a hypothesis is either rejected or not (there is no gradient of evidence for/against).[3]<p>[1] Gigerenzer, G (November 2004). "Mindless statistics". The Journal of Socio-Economics. 33 (5): 587–606. doi:10.1016/j.socec.2004.09.033<p>[2] 'The phrase "errors of the second kind", although apparently only a harmless piece of techinical jargon, is useful as indicating the type of mental confusion in which it was coined.' -Ronald Fisher. "Statistical Methods and Scientific Induction." Journal of the Royal Statistical Society. Series B (Methodological) Vol. 17, No. 1 (1955), pp. 69-78 <a href="https://www.jstor.org/stable/2983785" rel="nofollow">https://www.jstor.org/stable/2983785</a><p>[3] 'no test based upon the theory of probability can by itself provide any valuable evidence of the truth or falsehood of that hypothesis.' -Neyman, J; Pearson, E. S. (January 1, 1933). "On the Problem of the most Efficient Tests of Statistical Hypotheses". Phil. Trans. R. Soc. Lond. A. 231 (694–706): 289–337. doi:10.1098/rsta.1933.0009.
Cool, but I would call this a cheatsheet.
Almost the inverse of a cookbook, which I think of as a set of "how tos" for tasks you want to accomplish.
Quite useful indeed!
If I may, I'd love to see references for each concept.<p>I appreciate anyone can look "sample space" or "parametric inference" up on Google, but it'd take some time to find some authoritative source (especially for people like who do not work with stats every day).
It'd be awesome if I could see a "[1]" and a reference (or list of references), either online or offline, where the concept is defined.
I'm struggling to understand the CDF for the discrete uniform distribution (line 1 of the first page).
I think the equation is missing a "+1" in the denominator as otherwise it doesn't make sense.
Although it has been a while since I took a stats class, so I may be mistaken
Anyone have a recommendation on a good statistic textbook?<p>Everything I've tried has been absolutely horrible except for "An Introduction to Error Analysis" by John Taylor (yeah the classical mechanics guy). Unfortunately it's a bit basic...