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  2. Type I and type II errors - Wikipedia

    en.wikipedia.org/wiki/Type_I_and_type_II_errors

    In statistical hypothesis testing, a type I error, or a false positive, is the rejection of the null hypothesis when it is actually true. A type II error, or a false negative, is the failure to reject a null hypothesis that is actually false. [1] Type I error: an innocent person may be convicted. Type II error: a guilty person may be not convicted.

  3. False positives and false negatives - Wikipedia

    en.wikipedia.org/wiki/False_positives_and_false...

    The specificity of the test is equal to 1 minus the false positive rate. In statistical hypothesis testing, this fraction is given the Greek letter α, and 1 − α is defined as the specificity of the test. Increasing the specificity of the test lowers the probability of type I errors, but may raise the probability of type II errors (false ...

  4. 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 ...

  5. Multiple comparisons problem - Wikipedia

    en.wikipedia.org/wiki/Multiple_comparisons_problem

    For example, if one test is performed at the 5% level and the corresponding null hypothesis is true, there is only a 5% risk of incorrectly rejecting the null hypothesis. However, if 100 tests are each conducted at the 5% level and all corresponding null hypotheses are true, the expected number of incorrect rejections (also known as false ...

  6. Null hypothesis - Wikipedia

    en.wikipedia.org/wiki/Null_hypothesis

    Consider the following example. Given the test scores of two random samples, one of men and one of women, does one group score better than the other? A possible null hypothesis is that the mean male score is the same as the mean female score: H 0: μ 1 = μ 2. where H 0 = the null hypothesis, μ 1 = the mean of population 1, and μ 2 = the mean ...

  7. 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 ...

  8. Temporary Error 1 in AOL Mail

    help.aol.com/articles/temporary-error-1-in-aol-mail

    Accessing AOL Mail account simultaneously from 2 different browsers. Accessing AOL Mail on multiple devices simultaneously from more than one place. Possible fixes

  9. Error exponents in hypothesis testing - Wikipedia

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

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