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2. Confidence interval - Wikipedia

   en.wikipedia.org/wiki/Confidence_interval

   Informally, in frequentist statistics, a confidence interval ( CI) is an interval which is expected to typically contain the parameter being estimated. More specifically, given a confidence level (95% and 99% are typical values), a CI is a random interval which contains the parameter being estimated % of the time. [ 1][ 2] The confidence level ...

3. Margin of error - Wikipedia

   en.wikipedia.org/wiki/Margin_of_error

   For a confidence level, there is a corresponding confidence interval about the mean , that is, the interval [, +] within which values of should fall with probability . Precise values of z γ {\displaystyle z_{\gamma }} are given by the quantile function of the normal distribution (which the 68-95-99.7 rule approximates).

4. Standard error - Wikipedia

   en.wikipedia.org/wiki/Standard_error

   1.4 Independent and identically distributed random variables with random sample size. ... 25% , but for n = 6, the ... confidence interval; and; as the sample size ...

5. Prediction interval - Wikipedia

   en.wikipedia.org/wiki/Prediction_interval

   Prediction interval. In statistical inference, specifically predictive inference, a prediction interval is an estimate of an interval in which a future observation will fall, with a certain probability, given what has already been observed. Prediction intervals are often used in regression analysis . A simple example is given by a six-sided die ...

6. Sensitivity and specificity - Wikipedia

   en.wikipedia.org/wiki/Sensitivity_and_specificity

   A common way to do this is to state the binomial proportion confidence interval, often calculated using a Wilson score interval. Confidence intervals for sensitivity and specificity can be calculated, giving the range of values within which the correct value lies at a given confidence level (e.g., 95%). [26]

7. Coverage probability - Wikipedia

   en.wikipedia.org/wiki/Coverage_probability

   Coverage probability. In statistical estimation theory, the coverage probability, or coverage for short, is the probability that a confidence interval or confidence region will include the true value (parameter) of interest. It can be defined as the proportion of instances where the interval surrounds the true value as assessed by long-run ...