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  2. Power (statistics) - Wikipedia

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

    We define two hypotheses the null hypothesis, and the alternative hypothesis. If we design the test such that α is the significance level - being the probability of rejecting H 0 {\displaystyle H_{0}} when H 0 {\displaystyle H_{0}} is in fact true, then the power of the test is 1 - β where β is the probability of failing to reject H 0 ...

  3. Statistical hypothesis test - Wikipedia

    en.wikipedia.org/wiki/Statistical_hypothesis_test

    Here the null hypothesis is by default that two things are unrelated (e.g. scar formation and death rates from smallpox). [7] The null hypothesis in this case is no longer predicted by theory or conventional wisdom, but is instead the principle of indifference that led Fisher and others to dismiss the use of "inverse probabilities". [8]

  4. Type I and type II errors - Wikipedia

    en.wikipedia.org/wiki/Type_I_and_type_II_errors

    The consistent application by statisticians of Neyman and Pearson's convention of representing "the hypothesis to be tested" (or "the hypothesis to be nullified") with the expression H 0 has led to circumstances where many understand the term "the null hypothesis" as meaning "the nil hypothesis" – a statement that the results in question have ...

  5. Power of two - Wikipedia

    en.wikipedia.org/wiki/Power_of_two

    A power of two is a number of the form 2 n where n is an ... then p must be 124, which is impossible since by hypothesis p is not amongst the numbers 1, 2, 4, 8, 16 ...

  6. Sign test - Wikipedia

    en.wikipedia.org/wiki/Sign_test

    The sign test is a statistical test for consistent differences between pairs of observations, such as the weight of subjects before and after treatment. Given pairs of observations (such as weight pre- and post-treatment) for each subject, the sign test determines if one member of the pair (such as pre-treatment) tends to be greater than (or less than) the other member of the pair (such as ...

  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. Alternative hypothesis - Wikipedia

    en.wikipedia.org/wiki/Alternative_hypothesis

    The statement that is being tested against the null hypothesis is the alternative hypothesis. [2] Alternative hypothesis is often denoted as H a or H 1. In statistical hypothesis testing, to prove the alternative hypothesis is true, it should be shown that the data is contradictory to the null hypothesis. Namely, there is sufficient evidence ...

  9. Uniformly most powerful test - Wikipedia

    en.wikipedia.org/wiki/Uniformly_most_powerful_test

    In statistical hypothesis testing, a uniformly most powerful (UMP) test is a hypothesis test which has the greatest power among all possible tests of a given size α. For example, according to the Neyman–Pearson lemma , the likelihood-ratio test is UMP for testing simple (point) hypotheses.