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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. Confidence interval - Wikipedia

    en.wikipedia.org/wiki/Confidence_interval

    Factors affecting the width of the CI include the sample size, the variability in the sample, and the confidence level. [4] All else being the same, a larger sample produces a narrower confidence interval, greater variability in the sample produces a wider confidence interval, and a higher confidence level produces a wider confidence interval. [5]

  4. Margin of error - Wikipedia

    en.wikipedia.org/wiki/Margin_of_error

    For a confidence level, there is a corresponding confidence interval about the mean , that is, the interval [, +] within which values of should fall with probability . Precise values of z γ {\displaystyle z_{\gamma }} are given by the quantile function of the normal distribution (which the 68–95–99.7 rule approximates).

  5. 68–95–99.7 rule - Wikipedia

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

    In the social sciences, a result may be considered statistically significant if its confidence level is of the order of a two-sigma effect (95%), while in particle physics and astrophysics, there is a convention of requiring statistical significance of a five-sigma effect (99.99994% confidence) to qualify as a discovery. [3]

  6. Standard error - Wikipedia

    en.wikipedia.org/wiki/Standard_error

    Mathematically, the variance of the sampling mean distribution obtained is equal to the variance of the population divided by the sample size. This is because as the sample size increases, sample means cluster more closely around the population mean.

  7. Bootstrapping (statistics) - Wikipedia

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

    Confidence intervals should be valid or consistent, i.e., the probability a parameter is in a confidence interval with nominal level should be equal to or at least converge in probability to . The latter criteria is both refined and expanded using the framework of Hall. [ 41 ]

  8. File:Margin of error vs sample size and confidence level.svg

    en.wikipedia.org/wiki/File:Margin_of_error_vs...

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  9. Rule of three (statistics) - Wikipedia

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

    Comparison of the rule of three to the exact binomial one-sided confidence interval with no positive samples. In statistical analysis, the rule of three states that if a certain event did not occur in a sample with n subjects, the interval from 0 to 3/ n is a 95% confidence interval for the rate of occurrences in the population.