Concept

Seeding — where it appears

How a pseudorandom generator's starting state is derived from a number, which decides whether runs started at consecutive seeds are independent. A seeding that merely offsets the state leaves consecutive streams correlated, which quietly biases every measurement averaged across seeds.

Named by 2 essays across one field — each of them below, with the objects they name alongside it.

Twenty samples of 40, every one of them genuinely normal. Each panel is a quantile-quantile plot of 40 draws from a normal distribution. The worst point in the worst panel sits 0.87 standard deviations off the line. Anything a reader would reject here would be a false alarm.

The seed is part of the figure

Every other site in this fleet draws from a deterministic rule, so a figure either is or is not what it claims. Here the figures are samples, and a sample can be right by luck. That changes what a figure has to carry.

method · Seeds

Coverage of four nominal 95% intervals, n = 30. Computed exactly by summing over all 31 possible counts, not simulated. The Wald interval drops to 26.0% and is jagged everywhere; Clopper–Pearson never falls below 95% and pays for it in width.

Two routes to every number

A site about probability that only simulates has one route to each answer and no way to tell a right one from a plausible one. Every important number here is computed twice, by arithmetic that shares nothing, and the two are required to agree.

method · Routes

Named alongside it

The objects these essays reach for when they reach for this one.

Coverage Degrees of freedom Monte Carlo Numerical methods Pseudorandom generator Reproducibility Round trip Spectral test Verification

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