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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. In meta-analysis, which combines the data from several studies, homogeneity measures the differences or similarities between the several studies (see also Study heterogeneity).
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 ...
In social science, antipositivism (also interpretivism, negativism [citation needed] or antinaturalism) is a theoretical stance which proposes that the social realm cannot be studied with the methods of investigation utilized within the natural sciences, and that investigation of the social realm requires a different epistemology.
Semantic heterogeneity is when database schema or datasets for the same domain are developed by independent parties, resulting in differences in meaning and interpretation of data values. [1] Beyond structured data , the problem of semantic heterogeneity is compounded due to the flexibility of semi-structured data and various tagging methods ...
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.
Tissue heterogeneity affects commonly used, reference gene expression datasets such as the Genotype-Tissue Expression Project (GTEx). [2] Cancer samples often display varying degree of heterogeneity, because they consist of tumor cells of multiple subclones, immune cells, and other cell types.