Behavioral Research: Statistical Methods

Probability is either **long-run frequency** (frequentist) or **degree of belief** (Bayesian). PDFs describe continuous variables (probability of any *exact* value = 0); CDFs give cumulative probabilities. The **Central Limit Theorem** is the spell that makes everything else work: sample means → Normal as $n$ grows, regardless of population shape (provided finite variance). That is why parametric tests apply broadly — they assume normality of the *sampling distribution*, not of the raw data, and the CLT guarantees the former.