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In statistics, homogeneity and its opposite, heterogeneity, arise in describing the properties of a dataset, or several datasets.They relate to the validity of the often convenient assumption that the statistical properties of any one part of an overall dataset are the same as any other part.
The complementary notion is called heteroscedasticity, also known as heterogeneity of variance. The spellings homos k edasticity and heteros k edasticity are also frequently used. “Skedasticity” comes from the Ancient Greek word “skedánnymi”, meaning “to scatter”.
Statistical testing for a non-zero heterogeneity variance is often done based on Cochran's Q [13] or related test procedures. This common procedure however is questionable for several reasons, namely, the low power of such tests [14] especially in the very common case of only few estimates being combined in the analysis, [15] [7] as well as the specification of homogeneity as the null ...
Homogeneity and heterogeneity; only ' b ' is homogeneous Homogeneity and heterogeneity are concepts relating to the uniformity of a substance, process or image.A homogeneous feature is uniform in composition or character (i.e., color, shape, size, weight, height, distribution, texture, language, income, disease, temperature, radioactivity, architectural design, etc.); one that is heterogeneous ...
Although there are some variations, the traditional minimal group study consists of two phases. In the first phase, participants are randomly and anonymously divided into two groups (e.g., "Group A" and "Group B"), ostensibly on the basis of trivial criteria (e.g., preference for paintings or the toss of a coin).
The endogeneity problem is particularly relevant in the context of time series analysis of causal processes. It is common for some factors within a causal system to be dependent for their value in period t on the values of other factors in the causal system in period t − 1.
A diversity index is a method of measuring how many different types (e.g. species) there are in a dataset (e.g. a community).Diversity indices are statistical representations of different aspects of biodiversity (e.g. richness, evenness, and dominance), which are useful simplifications for comparing different communities or sites.
Methods for obtaining valid statistical inferences in the presence of unobserved heterogeneity include the instrumental variables method; multilevel models, including fixed effects and random effects models; and the Heckman correction for selection bias.