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Sample size determination or estimation is the act of choosing the number of observations or replicates to include in a statistical sample. The sample size is an important feature of any empirical study in which the goal is to make inferences about a population from a sample.
In statistics, Cochran's theorem, devised by William G. Cochran, [1] is a theorem used to justify results relating to the probability distributions of statistics that are used in the analysis of variance.
Download as PDF; Printable version ... The effective sample size, ... Cochran (1977) provides a formula for the proportional increase in variance due to deviation ...
Cochran's test, [1] named after William G. Cochran, is a one-sided upper limit variance outlier statistical test . The C test is used to decide if a single estimate of a variance (or a standard deviation ) is significantly larger than a group of variances (or standard deviations) with which the single estimate is supposed to be comparable.
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).
Cochran–Armitage test for trend; Cochran–Mantel–Haenszel statistics; Correspondence analysis; Cronbach's alpha; Diagnostic odds ratio; G-test; Generalized estimating equations; Generalized linear models; Krichevsky–Trofimov estimator; Kuder–Richardson Formula 20; Linear discriminant analysis; Multinomial distribution; Multinomial ...
Correction factor versus sample size n.. When the random variable is normally distributed, a minor correction exists to eliminate the bias.To derive the correction, note that for normally distributed X, Cochran's theorem implies that () / has a chi square distribution with degrees of freedom and thus its square root, / has a chi distribution with degrees of freedom.
In statistics, the Cochran–Mantel–Haenszel test (CMH) is a test used in the analysis of stratified or matched categorical data. It allows an investigator to test the association between a binary predictor or treatment and a binary outcome such as case or control status while taking into account the stratification. [ 1 ]