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  2. Homogeneity and heterogeneity (statistics) - Wikipedia

    en.wikipedia.org/wiki/Homogeneity_and...

    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. In meta-analysis, which combines the data from ...

  3. Homogeneity and heterogeneity - Wikipedia

    en.wikipedia.org/wiki/Homogeneity_and_heterogeneity

    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 ...

  4. Study heterogeneity - Wikipedia

    en.wikipedia.org/wiki/Study_heterogeneity

    Study heterogeneity. In statistics, (between-) study heterogeneity is a phenomenon that commonly occurs when attempting to undertake a meta-analysis. In a simplistic scenario, studies whose results are to be combined in the meta-analysis would all be undertaken in the same way and to the same experimental protocols.

  5. Homoscedasticity and heteroscedasticity - Wikipedia

    en.wikipedia.org/wiki/Homoscedasticity_and...

    In statistics, a sequence of random variables is homoscedastic (/ ˌhoʊmoʊskəˈdæstɪk /) 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. The spellings homoskedasticity and ...

  6. Research design - Wikipedia

    en.wikipedia.org/wiki/Research_design

    A research design typically outlines the theories and models underlying a project; the research question (s) of a project; a strategy for gathering data and information; and a strategy for producing answers from the data. [1] A strong research design yields valid answers to research questions while weak designs yield unreliable, imprecise or ...

  7. Difference in differences - Wikipedia

    en.wikipedia.org/wiki/Difference_in_differences

    Difference in differences (DID [1] or DD [2]) is a statistical technique used in econometrics and quantitative research in the social sciences that attempts to mimic an experimental research design using observational study data, by studying the differential effect of a treatment on a 'treatment group' versus a 'control group' in a natural experiment. [3]

  8. External validity - Wikipedia

    en.wikipedia.org/wiki/External_validity

    External validity is the validity of applying the conclusions of a scientific study outside the context of that study. [1] In other words, it is the extent to which the results of a study can generalize or transport to other situations, people, stimuli, and times. [2][3] Generalizability refers to the applicability of a predefined sample to a ...

  9. Design of experiments - Wikipedia

    en.wikipedia.org/wiki/Design_of_experiments

    The design of experiments, also known as experiment design or experimental design, is the design of any task that aims to describe and explain the variation of information under conditions that are hypothesized to reflect the variation. The term is generally associated with experiments in which the design introduces conditions that directly ...