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  2. Sample size determination - Wikipedia

    en.wikipedia.org/wiki/Sample_size_determination

    The table shown on the right can be used in a two-sample t-test to estimate the sample sizes of an experimental group and a control group that are of equal size, that is, the total number of individuals in the trial is twice that of the number given, and the desired significance level is 0.05. [4]

  3. Kaiser–Meyer–Olkin test - Wikipedia

    en.wikipedia.org/wiki/Kaiser–Meyer–Olkin_test

    The Kaiser–Meyer–Olkin (KMO) test is a statistical measure to determine how suited data is for factor analysis. The test measures sampling adequacy for each variable in the model and the complete model. The statistic is a measure of the proportion of variance among variables that might be common variance.

  4. Tolerance interval - Wikipedia

    en.wikipedia.org/wiki/Tolerance_interval

    A tolerance interval (TI) is a statistical interval within which, with some confidence level, a specified sampled proportion of a population falls. "More specifically, a 100×p%/100×(1−α) tolerance interval provides limits within which at least a certain proportion (p) of the population falls with a given level of confidence (1−α)."

  5. Design effect - Wikipedia

    en.wikipedia.org/wiki/Design_effect

    All of these factors should be considered when estimating and using design effect in practice. [4]: ... The effective sample size, defined by Kish in 1965, ...

  6. Completely randomized design - Wikipedia

    en.wikipedia.org/wiki/Completely_randomized_design

    k = number of factors (= 1 for these designs) L = number of levels; n = number of replications; and the total sample size (number of runs) is N = k × L × n. Balance dictates that the number of replications be the same at each level of the factor (this will maximize the sensitivity of subsequent statistical t- (or F-) tests).

  7. Blocking (statistics) - Wikipedia

    en.wikipedia.org/wiki/Blocking_(statistics)

    Let X 1 be dosage "level" and X 2 be the blocking factor furnace run. Then the experiment can be described as follows: k = 2 factors (1 primary factor X 1 and 1 blocking factor X 2) L 1 = 4 levels of factor X 1 L 2 = 3 levels of factor X 2 n = 1 replication per cell N = L 1 * L 2 = 4 * 3 = 12 runs. Before randomization, the design trials look like:

  8. Sampling (statistics) - Wikipedia

    en.wikipedia.org/wiki/Sampling_(statistics)

    Formulas, tables, and power function charts are well known approaches to determine sample size. Steps for using sample size tables: Postulate the effect size of interest, α, and β. Check sample size table [20] Select the table corresponding to the selected α; Locate the row corresponding to the desired power; Locate the column corresponding ...

  9. K-factor - Wikipedia

    en.wikipedia.org/wiki/K-factor

    K-factor (Elo rating system), a constant used in Elo rating system; K-factor (marketing), the growth rate of websites, apps, or a customer base; K-factor (sheet metal), the ratio of location of the neutral line to the material thickness; The K Factor, a fictional TV show within Harry Hill's TV Burp; Bondi k-factor, the "k" in Bondi k-calculus