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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. 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)."

  4. K factor (traffic engineering) - Wikipedia

    en.wikipedia.org/wiki/K_factor_(traffic_engineering)

    The K Factor also helps calculate the peak-to-daily ratio of traffic. K30 helps maintain a healthy volume to capacity ratio. [3] K50 and K100 will sometimes be seen. K50 and K100 will not use the 30th highest hourly traffic volumes but the 50th or 100th highest hourly traffic volume when calculating the K factor.

  5. Sampling (statistics) - Wikipedia

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

    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 to the estimated effect size. The intersection of the column and row is the minimum sample size ...

  6. Yates analysis - Wikipedia

    en.wikipedia.org/wiki/Yates_Analysis

    Before performing a Yates analysis, the data should be arranged in "Yates' order". That is, given k factors, the k th column consists of 2 (k - 1) minus signs (i.e., the low level of the factor) followed by 2 (k - 1) plus signs (i.e., the high level of the factor). For example, for a full factorial design with three factors, the design matrix is

  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. Design effect - Wikipedia

    en.wikipedia.org/wiki/Design_effect

    Table 1: Summary of notation ... , proposed by Kish in 1995, is the Design Effect Factor, abbreviated as (or also ... A related quantity is the effective sample size ...

  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