enow.com Web Search

Search results

  1. Results from the WOW.Com Content Network
  2. Type I and type II errors - Wikipedia

    en.wikipedia.org/wiki/Type_I_and_type_II_errors

    For example, if the p-value of a test statistic result is estimated at 0.0596, then there is a probability of 5.96% that we falsely reject H 0. Or, if we say, the statistic is performed at level α, like 0.05, then we allow to falsely reject H 0 at 5%. A significance level α of 0.05 is relatively common, but there is no general rule that fits ...

  3. Statistical hypothesis test - Wikipedia

    en.wikipedia.org/wiki/Statistical_hypothesis_test

    Null hypothesis (H 0) Positive data: Data that enable the investigator to reject a null hypothesis. Alternative hypothesis (H 1) Suppose the data can be realized from an N(0,1) distribution. For example, with a chosen significance level α = 0.05, from the Z-table, a one-tailed critical value of approximately 1.645 can be obtained.

  4. 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 ...

  5. False positives and false negatives - Wikipedia

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

    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 negatives that reject the alternative hypothesis when it is true). [a]

  6. Error exponents in hypothesis testing - Wikipedia

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

    Main page; Contents; Current events; Random article; About Wikipedia; Contact us; Help; Learn to edit; Community portal; Recent changes; Upload file

  7. One- and two-tailed tests - Wikipedia

    en.wikipedia.org/wiki/One-_and_two-tailed_tests

    In that case a data set of five heads (HHHHH), with sample mean of 1, has a / = chance of occurring, (5 consecutive flips with 2 outcomes - ((1/2)^5 =1/32). This would have p ≈ 0.03 {\displaystyle p\approx 0.03} and would be significant (rejecting the null hypothesis) if the test was analyzed at a significance level of α = 0.05 ...

  8. Jonckheere's trend test - Wikipedia

    en.wikipedia.org/wiki/Jonckheere's_Trend_Test

    Jonckheere suggested breaking the ties against the alternative hypothesis and then using exact tables. [1] In the current example where tied scores only appear in adjacent groups, the value of S is unchanged if the ties are broken against the alternative hypothesis. This may be verified by substituting 11 mph in place of 12 mph in the Bumped ...

  9. Hopkins statistic - Wikipedia

    en.wikipedia.org/wiki/Hopkins_statistic

    The Hopkins statistic (introduced by Brian Hopkins and John Gordon Skellam) is a way of measuring the cluster tendency of a data set. [1] It belongs to the family of sparse sampling tests. It acts as a statistical hypothesis test where the null hypothesis is that the data is generated by a Poisson point process and are thus uniformly randomly ...