Probability Distribution Guides

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

Starts with Normal Distribution.

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

Starts with Normal Distribution.

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

Starts with Normal Distribution.

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

Starts with Normal Distribution.

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

Starts with Normal Distribution.

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

Starts with Student's t Distribution.

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

Starts with Chi-Squared Distribution.

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

Starts with Exponential Distribution.

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

Starts with Binomial Distribution.

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

Starts with Normal Distribution.