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  2. Effect size - Wikipedia

    en.wikipedia.org/wiki/Effect_size

    In statistics, an effect size is a value measuring the strength of the relationship between two variables in a population, or a sample-based estimate of that quantity. It can refer to the value of a statistic calculated from a sample of data, the value of one parameter for a hypothetical population, or to the equation that operationalizes how statistics or parameters lead to the effect size ...

  3. Cohen's h - Wikipedia

    en.wikipedia.org/wiki/Cohen's_h

    In statistics, Cohen's h, popularized by Jacob Cohen, is a measure of distance between two proportions or probabilities. Cohen's h has several related uses: It can be used to describe the difference between two proportions as "small", "medium", or "large". It can be used to determine if the difference between two proportions is "meaningful".

  4. Probability of superiority - Wikipedia

    en.wikipedia.org/wiki/Probability_of_superiority

    In other words, the correlation is the difference between the common language effect size and its complement. For example, if the common language effect size is 60%, then the rank-biserial r equals 60% minus 40%, or r = 0.20. The Kerby formula is directional, with positive values indicating that the results support the hypothesis.

  5. Category:Effect size - Wikipedia

    en.wikipedia.org/wiki/Category:Effect_size

    Pages in category "Effect size" The following 6 pages are in this category, out of 6 total. This list may not reflect recent changes. ... Statistics; Cookie statement;

  6. Strictly standardized mean difference - Wikipedia

    en.wikipedia.org/wiki/Strictly_standardized_mean...

    The size of the compound effect is represented by the magnitude of difference between a test compound and a negative reference group with no specific inhibition/activation effects. A compound with a desired size of effects in an HTS screen is called a hit. The process of selecting hits is called hit selection.

  7. Design effect - Wikipedia

    en.wikipedia.org/wiki/Design_effect

    In various papers, when cluster sizes are not equal, the above formula is also used with as the average cluster size (which is also sometimes denoted as ¯). [ 36 ] [ 28 ] : 105 In such cases, Kish's formula (using the average cluster weight) serves as a conservative (upper bound) of the exact design effect.

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  9. Z-factor - Wikipedia

    en.wikipedia.org/wiki/Z-factor

    The Z-factor is a measure of statistical effect size. It has been proposed for use in high-throughput screening (HTS), where it is also known as Z-prime, [ 1 ] to judge whether the response in a particular assay is large enough to warrant further attention.