Tutorial slides

A ton of short useful tutorials here.

Because of my personal beliefs (one could say biases) I have never accepted the idea that there's a meaningful underlying factor of intelligence g. Cosma Shalizi doesn't buy the existing evidence either.

. . . the case for g rests on a statistical technique, factor analysis, which works solely on correlations between tests. Factor analysis is handy for summarizing data, but can't tell us where the correlations came from; it always says that there is a general factor whenever there are only positive correlations. The appearance of g is a trivial reflection of that correlation structure. A clear example, known since 1916, shows that factor analysis can give the appearance of a general factor when there are actually many thousands of completely independent and equally strong causes at work. Heritability doesn't distinguish these alternatives either. Exploratory factor analysis being no good at discovering causal structure, it provides no support for the reality of g.

. . .

To paraphrase Hume:

When we run over libraries, persuaded of these principles, what havoc must we make? If we take in our hand any paper; of macroeconomics or correlational psychology, for instance; let us ask, Does it draw its causal inferences from observations with consistent methods? No. Does it draw its causal inferences from experiments, controlled or randomized? No. Commit it then to the recycling bin: for it can contain nothing but sophistry and illusion.

It's increasingly clear that I need to be stats-literate. Here are the first two posts in a series that Carson Chow is planning on Bayesian statistics, the Markov chain Monte Carlo method, and fitting models to data.

• MCMC and fitting models to data
• Bayesian model comparison

Other relevant links:

• Bayesian statistics can be used for Popperian model checking even when there isn't an alternative model for comparison