posterior samples

I probably should have put this post up earlier because it’s now a huge collection of stuff from the last month. Here we go!

It appears that Hilary Parker and I have similar (but by no means identical) work setups for doing stats (or at least we did two years ago). It’s never too late to come up with a sensible way of organising your work and collection of references/downloaded papers.

Applied statisticians should probably teach scientists what it is we do, rather than just the mathematics behind statistics. This is a difference I’ve noticed between SEB113 and more traditional statistics classes; we spend a lot less time discussion F distributions and a lot more time on model development and visualisation.

Speaking of visualisation, here’s a really great article on visualisation and how we can use small multiples and colour, shape, etc. to highlight the interesting differences so that it’s very clear what our message is.

Jeff Leek has compiled a list of some of the most awesome data people on Twitter who happen to be female.

In the ongoing crusade against abuse of p-values, we may want to instead focus on reproducibility to show that our results say what we say they do. Andrew Gelman and Eric Loken have an article in The American Statistician reminding us that p-values have a context and we need to be aware of issues like sample size, p-hacking, multiple comparisons, etc.

 

 

 

 

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