Statistics & Inference

Distributions · CLT · Testing · Bayes

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How to reason from a sample to a population — distributions, the CLT, hypothesis testing, p-values, confidence intervals, and Bayesian thinking.

By the end, you can

Distinguish population from sample and descriptive from inferential statistics.
Compute the mean, median, mode, variance, and standard deviation of a small dataset.
State the empirical rule (68-95-99.7) and apply it to normal distributions.
Explain the Central Limit Theorem and compute the standard error.
Interpret a confidence interval correctly and identify the common misconception.
Define the p-value and explain what it does and does not say.
Distinguish Type I from Type II errors and define statistical power.
Explain why correlation does not imply causation and identify confounders.
Describe how least-squares regression minimizes residuals.
Apply Bayes' theorem to a screening scenario and explain why base rates dominate.

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