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  2. Dixon's Q test - Wikipedia

    en.wikipedia.org/wiki/Dixon's_Q_test

    However, at 95% confidence, Q = 0.455 < 0.466 = Q table 0.167 is not considered an outlier. McBane [1] notes: Dixon provided related tests intended to search for more than one outlier, but they are much less frequently used than the r 10 or Q version that is intended to eliminate a single outlier.

  3. False coverage rate - Wikipedia

    en.wikipedia.org/wiki/False_coverage_rate

    This procedure is sharp in the sense that for some configurations, the FCR approaches q. 1. Sort the p values used for testing the m hypotheses regarding the parameters, P(1) ≤ • • • ≤P(m). 2. Calculate R = max{i : P(i) ≤ i • q/m}. 3. Select the R parameters for which P(i) ≤ Rq/m, corresponding to the rejected hypotheses. 4.

  4. Confidence interval - Wikipedia

    en.wikipedia.org/wiki/Confidence_interval

    The confidence interval can be expressed in terms of a long-run frequency in repeated samples (or in resampling): "Were this procedure to be repeated on numerous samples, the proportion of calculated 95% confidence intervals that encompassed the true value of the population parameter would tend toward 95%." [19] The confidence interval can be ...

  5. Circular error probable - Wikipedia

    en.wikipedia.org/wiki/Circular_error_probable

    Another is the R95, which is the radius of the circle where 95% of the values would fall, a 95% confidence interval. The concept of CEP also plays a role when measuring the accuracy of a position obtained by a navigation system, such as GPS or older systems such as LORAN and Loran-C.

  6. Coverage probability - Wikipedia

    en.wikipedia.org/wiki/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 frequency. [1]

  7. 68–95–99.7 rule - Wikipedia

    en.wikipedia.org/wiki/68–95–99.7_rule

    In statistics, the 68–95–99.7 rule, also known as the empirical rule, and sometimes abbreviated 3sr or 3 σ, is a shorthand used to remember the percentage of values that lie within an interval estimate in a normal distribution: approximately 68%, 95%, and 99.7% of the values lie within one, two, and three standard deviations of the mean ...

  8. 97.5th percentile point - Wikipedia

    en.wikipedia.org/wiki/97.5th_percentile_point

    The approximate value of this number is 1.96, meaning that 95% of the area under a normal curve lies within approximately 1.96 standard deviations of the mean. Because of the central limit theorem, this number is used in the construction of approximate 95% confidence intervals. Its ubiquity is due to the arbitrary but common convention of using ...

  9. Bootstrapping (statistics) - Wikipedia

    en.wikipedia.org/wiki/Bootstrapping_(statistics)

    So that with a sample of 20 points, 90% confidence interval will include the true variance only 78% of the time. [44] The basic / reverse percentile confidence intervals are easier to justify mathematically [45] [42] but they are less accurate in general than percentile confidence intervals, and some authors discourage their use. [42]