Probability Distribution Guides

Use this guide to quickly decide whether normal or Student t is the right model for your inference setup.

A concise reference for interpreting probability mass, probability density, and cumulative probability functions.

A practical guide to converting between probability levels and data thresholds.

Tail probabilities capture exceedance risk and are central to reliability, anomalies, and statistical testing.

A quick framework for estimating parameters and validating model fit in applied analysis.

A practical walkthrough of the three main t-test variants, their assumptions, and when each one applies.

A practical guide to the two main chi-squared tests: goodness-of-fit for checking distributional assumptions and independence for contingency tables.

An applied introduction to the probability distributions and functions used in reliability engineering and survival analysis.

A practical guide to choosing between discrete and continuous probability models based on your variable type and analysis goals.

A structured decision framework for selecting probability distributions based on data characteristics, domain knowledge, and modeling goals.