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  2. Error analysis (linguistics) - Wikipedia

    en.wikipedia.org/wiki/Error_analysis_(linguistics)

    Chomsky (1965) made a distinguishing explanation of competence and performance on which, later on, the identification of mistakes and errors will be possible, Chomsky stated that ‘’We thus make a fundamental distinction between competence (the speaker-hearer's knowledge of his language) and performance (the actual use of language in concrete situations)’’ ( 1956, p. 4).

  3. Type III error - Wikipedia

    en.wikipedia.org/wiki/Type_III_error

    In 1970, L. A. Marascuilo and J. R. Levin proposed a "fourth kind of error" – a "type IV error" – which they defined in a Mosteller-like manner as being the mistake of "the incorrect interpretation of a correctly rejected hypothesis"; which, they suggested, was the equivalent of "a physician's correct diagnosis of an ailment followed by the ...

  4. Multiple comparisons problem - Wikipedia

    en.wikipedia.org/wiki/Multiple_comparisons_problem

    A normal quantile plot for a simulated set of test statistics that have been standardized to be Z-scores under the null hypothesis. The departure of the upper tail of the distribution from the expected trend along the diagonal is due to the presence of substantially more large test statistic values than would be expected if all null hypotheses were true.

  5. Statistical hypothesis test - Wikipedia

    en.wikipedia.org/wiki/Statistical_hypothesis_test

    An example of Neyman–Pearson hypothesis testing (or null hypothesis statistical significance testing) can be made by a change to the radioactive suitcase example. If the "suitcase" is actually a shielded container for the transportation of radioactive material, then a test might be used to select among three hypotheses: no radioactive source ...

  6. Lindley's paradox - Wikipedia

    en.wikipedia.org/wiki/Lindley's_paradox

    Naaman [3] proposed an adaption of the significance level to the sample size in order to control false positives: α n, such that α n = n − r with r > 1/2. At least in the numerical example, taking r = 1/2, results in a significance level of 0.00318, so the frequentist would not reject the null hypothesis, which is in agreement with the ...

  7. Neyman–Pearson lemma - Wikipedia

    en.wikipedia.org/wiki/Neyman–Pearson_lemma

    Neyman–Pearson lemma [5] — Existence:. If a hypothesis test satisfies condition, then it is a uniformly most powerful (UMP) test in the set of level tests.. Uniqueness: If there exists a hypothesis test that satisfies condition, with >, then every UMP test in the set of level tests satisfies condition with the same .

  8. Permutation test - Wikipedia

    en.wikipedia.org/wiki/Permutation_test

    The hypothesis is that the mean of the first distribution is higher than the mean of the second; the null hypothesis is that both groups of samples are drawn from the same distribution. There are 126 distinct ways to put 4 values into one group and 5 into another (9-choose-4 or 9-choose-5).

  9. Error exponents in hypothesis testing - Wikipedia

    en.wikipedia.org/wiki/Error_exponents_in...

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