A friendly, informative, and light guide to some of the common issues in frequentist statistics/NHST. However, Baysian methods aren't even touched upon, which would address the whole, you know, P-Value issue.
From this, I conclude that the Author doesn't live in the Baye Area.
I liked the examples you used in your articles. [I had seen the dead salmon paper before, but your way of presenting it was funny.]
Not sure how one present practical bayes stats at the low level either- Kruschke does a great job, but it takes him most of a text book, and a whole lot of different coins from different mints to do it.
Posterior distributions. From them you can derive confidence intervals (or credible intervals as called within Bayesian stats) for your estimates and also set up tests. I'm not that familiar with testing within the Bayesian regime though so if someone else could add on this I would also be interested.
Kruschke goes over decision rules for dealing with the null hypothesis within the context of a 95% highest probability density interval, among other things.
well, there are other problems with the Bayesian approach. Look into uninformative priors for example. Bayesian stats are really powerful for some tasks and frequentist in others. I think it is always good to know a bit of both and when to use what.
