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  2. Kendall rank correlation coefficient - Wikipedia

    en.wikipedia.org/wiki/Kendall_rank_correlation...

    For a 2-tailed test, multiply that number by two to obtain the p-value. If the p-value is below a given significance level, one rejects the null hypothesis (at that significance level) that the quantities are statistically independent. Numerous adjustments should be added to when accounting for ties.

  3. Confidence interval - Wikipedia

    en.wikipedia.org/wiki/Confidence_interval

    A 95% confidence level does not mean that 95% of the sample data lie within the confidence interval. A 95% confidence level does not mean that there is a 95% probability of the parameter estimate from a repeat of the experiment falling within the confidence interval computed from a given experiment. [25]

  4. Dixon's Q test - Wikipedia

    en.wikipedia.org/wiki/Dixon's_Q_test

    To apply a Q test for bad data, arrange the data in order of increasing values and calculate Q as defined: Q = gap range {\displaystyle Q={\frac {\text{gap}}{\text{range}}}} Where gap is the absolute difference between the outlier in question and the closest number to it.

  5. Statistical hypothesis test - Wikipedia

    en.wikipedia.org/wiki/Statistical_hypothesis_test

    The p-value is the probability that a test statistic which is at least as extreme as the one obtained would occur under the null hypothesis. At a significance level of 0.05, a fair coin would be expected to (incorrectly) reject the null hypothesis (that it is fair) in 1 out of 20 tests on average.

  6. Student's t-test - Wikipedia

    en.wikipedia.org/wiki/Student's_t-test

    From the t-test, the difference between the group means is 6-2=4. From the regression, the slope is also 4 indicating that a 1-unit change in drug dose (from 0 to 1) gives a 4-unit change in mean word recall (from 2 to 6). The t-test p-value for the difference in means, and the regression p-value for the slope, are both 0.00805. The methods ...

  7. Equivalence test - Wikipedia

    en.wikipedia.org/wiki/Equivalence_test

    Equivalence tests are a variety of hypothesis tests used to draw statistical inferences from observed data. In these tests, the null hypothesis is defined as an effect large enough to be deemed interesting, specified by an equivalence bound. The alternative hypothesis is any effect that is less extreme than said equivalence bound.

  8. Goodness of fit - Wikipedia

    en.wikipedia.org/wiki/Goodness_of_fit

    The resulting value can be compared with a chi-square distribution to determine the goodness of fit. The chi-square distribution has ( k − c ) degrees of freedom , where k is the number of non-empty bins and c is the number of estimated parameters (including location and scale parameters and shape parameters) for the distribution plus one.

  9. Duncan's new multiple range test - Wikipedia

    en.wikipedia.org/wiki/Duncan's_new_multiple_range...

    The new multiple range test proposed by Duncan makes use of special protection levels based upon degrees of freedom. Let γ 2 , α = 1 − α {\displaystyle \gamma _{2,\alpha }={1-\alpha }} be the protection level for testing the significance of a difference between two means; that is, the probability that a significant difference between two ...

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