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  2. Kurtosis - Wikipedia

    en.wikipedia.org/wiki/Kurtosis

    For a sample of n values, a method of moments estimator of the population excess kurtosis can be defined as = = = (¯) [= (¯)] where m 4 is the fourth sample moment about the mean, m 2 is the second sample moment about the mean (that is, the sample variance), x i is the i th value, and ¯ is the sample mean.

  3. D'Agostino's K-squared test - Wikipedia

    en.wikipedia.org/wiki/D'Agostino's_K-squared_test

    In the following, { x i } denotes a sample of n observations, g 1 and g 2 are the sample skewness and kurtosis, m j ’s are the j-th sample central moments, and ¯ is the sample mean. Frequently in the literature related to normality testing , the skewness and kurtosis are denoted as √ β 1 and β 2 respectively.

  4. Higher-order statistics - Wikipedia

    en.wikipedia.org/wiki/Higher-order_statistics

    HOS are particularly used in the estimation of shape parameters, such as skewness and kurtosis, as when measuring the deviation of a distribution from the normal distribution. In statistical theory , one long-established approach to higher-order statistics, for univariate and multivariate distributions is through the use of cumulants and joint ...

  5. Jarque–Bera test - Wikipedia

    en.wikipedia.org/wiki/Jarque–Bera_test

    In statistics, the Jarque–Bera test is a goodness-of-fit test of whether sample data have the skewness and kurtosis matching a normal distribution. The test is named after Carlos Jarque and Anil K. Bera. The test statistic is always nonnegative. If it is far from zero, it signals the data do not have a normal distribution.

  6. Geometric distribution - Wikipedia

    en.wikipedia.org/wiki/Geometric_distribution

    An example of a geometric distribution arises from rolling a six-sided die until a "1" appears. Each roll is independent with a 1 / 6 {\displaystyle 1/6} chance of success. The number of rolls needed follows a geometric distribution with p = 1 / 6 {\displaystyle p=1/6} .

  7. L-moment - Wikipedia

    en.wikipedia.org/wiki/L-moment

    For instance, the Laplace distribution has a kurtosis of 6 and weak exponential tails, but a larger 4th L-moment ratio than e.g. the student-t distribution with d.f.=3, which has an infinite kurtosis and much heavier tails. As an example consider a dataset with a few data points and one outlying data value.

  8. Washington Post admits science behind puberty blockers and ...

    www.aol.com/news/washington-post-admits-science...

    The Washington Post argued in a new editorial that the benefits of gender-transition treatments for minors like puberty blockers have not been scientifically proven. "Treatment results that look ...

  9. Fat-tailed distribution - Wikipedia

    en.wikipedia.org/wiki/Fat-tailed_distribution

    A fat-tailed distribution is a probability distribution that exhibits a large skewness or kurtosis, relative to that of either a normal distribution or an exponential distribution. [when defined as?] In common usage, the terms fat-tailed and heavy-tailed are sometimes synonymous; fat-tailed is sometimes also defined as a subset of heavy-tailed ...