Distribution Models

01Distribution Models

Choosing a Distribution

Choose between a binomial count and a normal measurement using the situation and data.

Begin with what the variable records

Binomial: a count

Use X∼B(n,p)X\sim\mathrm B(n,p) to count successes in a fixed number of trials. The trials must be independent, classified as success/failure, and have the same success probability.

Example: the number of components, among 1515 tested independently, that pass the same quality check.

Normal: a measurement

Use M∼N(μ,σ2)M\sim\mathrm N(\mu,\sigma^2) for continuous measurements when a symmetric, bell-shaped model is reasonable.

Example: component lengths from a stable process, if their histogram is approximately symmetric and bell-shaped.

A histogram can support a normal model. With more observations from a normal population, its outline should become smoother and resemble the normal density curve. A measurement being continuous is not, by itself, enough to justify normality.

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