The companion web site for Serious Stats is now live:

http://www.palgrave.com/psychology/baguley/

The web site includes:

- a free sample chapter (Chapter 15: Contrasts)

- data sets

- R scripts

- 5 online supplements (for meta-analysis, multiple imputation, replication probabilities, pseudo-R squared and loglinear models)

Also don't forget the Serious stats blog to accompany the book.

This is a quick follow up to my earlier post that discussed how to graph CIs for within-subjects (repeated measures) ANOVA designs. My forthcoming book Serious stats describes how to do this for between-subjects designs (a much simpler problem).

I finally found some time to take a closer look at p curves. I haven't had a chance to follow-up my simulations (and probably won't for a few weeks if not months), but I have had time to think through the ideas the p curve approach raises based on some of the comments I've received and a brief exchange with Uri Simonsohn (who has answered a few of my questions).

First, I got a couple of things at least partly wrong.

I have finally got around to posting the R code for my p curve simulation. Those familiar with R will realize how crude it is (I've been caught up with other urgent stuff and had no time to explore further). You are welcome to play with (and improve!) the code. Changing delta will alter the (at present) fixed effect size. It would be more realistic to vary this (and the sample sizes).
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