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Published equation contexts

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

Why this formula appears here

If a real number belongs in the audit-trail conversation, it is a threshold, not a measurement, and it should be written 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 rate is actually below a threshold, especially at the sample sizes (…

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z0.95z_{0.95}

Symbol z_0.95

z0z_0.95 is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.

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nn

Symbol n

n occurs below the fraction bar. The quantity above the bar is divided by this expression; zero is excluded as a denominator.

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How to interpret it

With a fixed numerator, increasing a nonzero denominator reduces the fraction.

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Published contexts (1)

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

Equation 4 · Policy & Regulation

AI Governance and Regulation in Practice: An Advanced Technical Guide

This equation states a bound: one expression must stay on the indicated side of the other under the article’s assumptions.

If a real number belongs in the audit-trail conversation, it is a threshold, not a measurement, and it should be written 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 rate is actually below a threshold, especially at the sample sizes (…

Meanings in this article

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