The internet abounds nowadays with lots of helpful resources for learning data science, programming, etc. For classical statistics however, I find most of the popular online resources are fairly cursory. My hunch is that the dearth of accessible, detailed resources is that nowadays statistics for most professionals boils down to A/B testing e.g. the new name for Stats 101 hypothesis testing, and predictive machine learning models. This leaves people like me who work more on the inference side high and dry when you find yourself wondering why a particular method isn’t working or if you’re trying to improve your inference for a future analysis.

Udemy and the like are helpful as a starting point when learning about new statistical methods, but I find individual blog posts, forums like crossvalidated, and (unfortunately :P) academic papers are the best resources for insightful discussions by experts on the finer details of statistical methods.

This page is my attempty to compile helpful, lesser known resources in one place. I’ve tried to only select resources that were approachable enough so that I can understand them, and therefore actually useful to my work as a statistician.

General Statistics Stuff

“Basic” hypothesis testing, Ordinary Gaussian linear regression and its modifications, variable coding, power calculations, and other general philosophical considerations.

Analyzing Experimental Data/Post-hoc comparisons

Focusing on estimating marginal means and multiplicity adjustments for experimental data. The Mixed Effects models section below also can be used to analyze experimental data (the resources from Keith Lohse are a good place to start).


General considerations on GLMs, what they mean, how they are computed, deciding on distributions and link functions, etc.

Mixed Effects Models

LMMs and GLMMs, and their various considerations. This topic can get very hairy, so there are a ton of resources out there. Some are more helpful than others.


Marginal models, when you should use them, and how they stack up conceptually with other models like GLMMs.

Bayesian Statistics

Emphasis on McElreath’s statistical rethinking course.

Survival Models

Robust Statistics

Nonlinear modeling (nls, GAMS, NLMMs)

R Programming and Shiny

If you are subscribed to Shiny tags in LinkedIn, you’ve probably seen this guy evangelizing his “truly open source” alternative to Posit Connect and Appsilon, to be fair, he probably knows what he’s doing.

Miscellaneous Interesting Things

Basically like the general statistics section up above, but this section goes into more advanced “niche” topics.

Nightmares from MS program

Things that remind me of probability classes in grad school. Surprisingly enough, grad school probability (beyond knowing pbinom and the like) can occasionally be pretty useful in a workplace context, though I prefer to estimate probabilities through simulation when needed.