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  2. 68–95–99.7 rule - Wikipedia

    en.wikipedia.org/wiki/68–95–99.7_rule

    The "68–95–99.7 rule" is often used to quickly get a rough probability estimate of something, given its standard deviation, if the population is assumed to be normal. It is also used as a simple test for outliers if the population is assumed normal, and as a normality test if the population is potentially not normal.

  3. Outlier - Wikipedia

    en.wikipedia.org/wiki/Outlier

    Even when a normal distribution model is appropriate to the data being analyzed, outliers are expected for large sample sizes and should not automatically be discarded if that is the case. [26] Instead, one should use a method that is robust to outliers to model or analyze data with naturally occurring outliers. [26]

  4. Peirce's criterion - Wikipedia

    en.wikipedia.org/wiki/Peirce's_criterion

    The following Python code returns x-squared values for a given N (first column) and n (top row) in Table 1 (m = 1) and Table 2 (m = 2) of Gould 1855. [5] Due to the Newton-method of iteration, look-up tables, such as N versus log Q (Table III in Gould, 1855) and x versus log R (Table III in Peirce, 1852 and Table IV in Gould, 1855) are no ...

  5. Chauvenet's criterion - Wikipedia

    en.wikipedia.org/wiki/Chauvenet's_criterion

    The idea behind Chauvenet's criterion finds a probability band that reasonably contains all n samples of a data set, centred on the mean of a normal distribution.By doing this, any data point from the n samples that lies outside this probability band can be considered an outlier, removed from the data set, and a new mean and standard deviation based on the remaining values and new sample size ...

  6. Sample maximum and minimum - Wikipedia

    en.wikipedia.org/wiki/Sample_maximum_and_minimum

    The sample maximum and minimum are the least robust statistics: they are maximally sensitive to outliers.. This can either be an advantage or a drawback: if extreme values are real (not measurement errors), and of real consequence, as in applications of extreme value theory such as building dikes or financial loss, then outliers (as reflected in sample extrema) are important.

  7. Opinion - Surprises, outliers, oddities: What to anticipate ...

    www.aol.com/opinion-surprises-outliers-oddities...

    Opinion - Surprises, outliers, oddities: What to anticipate in the campaign’s closing days W. Joseph Campbell, opinion contributor October 21, 2024 at 7:00 AM

  8. Op-Ed: Trump woke up the silent majority - AOL

    www.aol.com/op-ed-trump-woke-silent-112200591.html

    President Donald Trump addresses the audience after the inaugural parade during the 60th Presidential Inauguration at Capital One Arena in Washington, D.C. on Jan. 20, 2025.

  9. Sample size determination - Wikipedia

    en.wikipedia.org/wiki/Sample_size_determination

    For B = 10% one requires n = 100, for B = 5% one needs n = 400, for B = 3% the requirement approximates to n = 1000, while for B = 1% a sample size of n = 10000 is required. These numbers are quoted often in news reports of opinion polls and other sample surveys .