# How to teach probability

#42

Super suggestions. Thanks! I only take issue with degree of belief which I don’t have much of a problem with. I hope I have time to make the changes.

#43

I’ve read the course some more. Great stuff
Question: the course describes different approaches in statistics and is critical of frequentist methods and more positive of bayesian methods. Nevertheless, nearly all of the techniques described in the course are (seem?) frequentist. For instance: sample size calculations, comparing group means, and so on. How about introducing the BEST package instead of the frequentist t-test? Here’s the blurb:

The BEST package provides a Bayesian alternative to a t test, providing …complete distributions of credible values for the effect size, group means and their difference, standard deviations and their difference, and the
normality of the data. …The decision rule can accept the null value (unlike traditional t tests) when certainty in the estimate is high (unlike Bayesian model comparison using Bayes factors). The package also provides methods to estimate statistical power for various research goals.

Just a thought.

#44

I could not agree more. The BEST approach, unlike peeking at the data to see which assumptions of a t-test are somewhat likely to hold then choosing an equal-variance vs. an unequal-variance t-test, is honest. The frequentist approach, when you don’t know whether variances are equal or that the distribution is Gaussian, results in inaccurate p-values and confidence limits. The Bayesian approach results in accurate credible intervals and posterior probabilities that take into account that we don’t know if the variances are equal or that the data are Gaussian.

#45

What a great topic. Even reading graphs and medical tests are a challenge for some physicians. They seem to echo what is taught to them by representatives of medical products. That is the statistics that they are apprised of.

#46

Stumbled upon this. https://iase-web.org/documents/papers/icots6/3f1_albe.pdf