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I² relates the heterogeneity variance's magnitude to the size of the individual estimates' variances (squared standard errors); with this normalisation however, it is not quite obvious what exactly would constitute "small" or "large" amounts of heterogeneity. For a constant heterogeneity (τ), the availability of smaller or larger studies ...
In statistics, a sequence of random variables is homoscedastic (/ ˌ h oʊ m oʊ s k ə ˈ d æ s t ɪ k /) if all its random variables have the same finite variance; this is also known as homogeneity of variance. The complementary notion is called heteroscedasticity, also known as heterogeneity of variance.
Cultural consensus theory assumes that cultural beliefs are learned and shared across people and that there is a common understanding of what the world and society are all about. [2] Since the amount of information in a culture is too large for any one individual to master, individuals know different subsets of the cultural knowledge and vary ...
Funnel plots, introduced by Light and Pillemer in 1984 [1] and discussed in detail by Matthias Egger and colleagues, [2] [3] are useful adjuncts to meta-analyses. A funnel plot is a scatterplot of treatment effect against a measure of study precision. It is used primarily as a visual aid for detecting bias or systematic heterogeneity.
The lists are commonly used in economics literature to compare the levels of ethnic, cultural, linguistic and religious fractionalization in different countries. [1] [2] Fractionalization is the probability that two individuals drawn randomly from the country's groups are not from the same group (ethnic, religious, or whatever the criterion is).
In statistics, Bartlett's test, named after Maurice Stevenson Bartlett, [1] is used to test homoscedasticity, that is, if multiple samples are from populations with equal variances. [2] Some statistical tests, such as the analysis of variance, assume that variances are equal across groups or samples, which can be checked with Bartlett's test.
The two graphics illustrate sampling distributions of polygenic scores and the predictive ability of stratified sampling on polygenic risk score with increasing age. + The left panel shows how risk—(the standardized PRS on the x-axis)—can separate 'cases' (i.e., individuals with a certain disease, (red)) from the 'controls' (individuals without the disease, (blue)).
[1] [2] [3] It is named after William Gemmell Cochran. Cochran's Q test should not be confused with Cochran's C test, which is a variance outlier test. Put in simple technical terms, Cochran's Q test requires that there only be a binary response (e.g. success/failure or 1/0) and that there be more than 2 groups of the same size.