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Equation 4 · Part 4 · AI Governance and Regulation in Practice: An Advanced Technical Guide

Symbol varepsilon

p^+z0.95p^(1−p^)n≤ε\hat{p} + z_{0.95}\sqrt{\frac{\hat{p}(1-\hat{p})}{n}} \le \varepsilon
ε\varepsilon

What this part means

the tolerance.

Its job in the formula

varepsilon is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.

Where the article explains it

A deployer setting an acceptance gate on a generative system’s confabulation rate might define a simple pass condition against a sampled evaluation set of size n , tolerance ε\varepsilon , and observed error rate p^\hat{p} : p^+z0.95p^(1−p^)n≤ε\hat{p} + z_{0.95}\sqrt{\frac{\hat{p}(1-\hat{p})}{n}} \le \varepsilon.

The passage around this formula

…as a model rather than asserted as fact. A deployer setting an acceptance gate on a generative system’s confabulation rate might define a simple pass condition against a sampled evaluation set of size n , tolerance ε\varepsilon , and observed error rate p^\hat{p} : p^+z0.95p^(1−p^)n≤ε\hat{p} + z_{0.95}\sqrt{\frac{\hat{p}(1-\hat{p})}{n}} \le \varepsilon. This is a standard one-sided Wald confidence bound, included here only because it exposes a real assumption practitioners often skip: a raw sampled error rate without its confidence interval says nothing about whether the true…

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Learn the underlying idea

A variable is a named place for a value. Its letter is a local label: x can mean position in one formula and a data point in another.

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Sources cited in the article section

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