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  2. Validity (statistics) - Wikipedia

    en.wikipedia.org/wiki/Validity_(statistics)

    Validity [5] of an assessment is the degree to which it measures what it is supposed to measure. This is not the same as reliability, which is the extent to which a measurement gives results that are very consistent. Within validity, the measurement does not always have to be similar, as it does in reliability.

  3. 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 ...

  4. Empirical research - Wikipedia

    en.wikipedia.org/wiki/Empirical_research

    Accurate analysis of data using standardized statistical methods in scientific studies is critical to determining the validity of empirical research. Statistical formulas such as regression, uncertainty coefficient, t-test, chi square, and various types of ANOVA (analyses of variance) are fundamental to forming logical, valid conclusions.

  5. Internal validity - Wikipedia

    en.wikipedia.org/wiki/Internal_validity

    Internal validity, therefore, is more a matter of degree than of either-or, and that is exactly why research designs other than true experiments may also yield results with a high degree of internal validity. In order to allow for inferences with a high degree of internal validity, precautions may be taken during the design of the study.

  6. Construct validity - Wikipedia

    en.wikipedia.org/wiki/Construct_validity

    Construct validity concerns how well a set of indicators represent or reflect a concept that is not directly measurable. [1] [2] [3] Construct validation is the accumulation of evidence to support the interpretation of what a measure reflects.

  7. Oversampling and undersampling in data analysis - Wikipedia

    en.wikipedia.org/wiki/Oversampling_and_under...

    Overabundance of already collected data became an issue only in the "Big Data" era, and the reasons to use undersampling are mainly practical and related to resource costs. Specifically, while one needs a suitably large sample size to draw valid statistical conclusions, the data must be cleaned before it can be used. Cleansing typically ...

  8. Systematic review - Wikipedia

    en.wikipedia.org/wiki/Systematic_review

    A systematic review is a scholarly synthesis of the evidence on a clearly presented topic using critical methods to identify, define and assess research on the topic. [1] A systematic review extracts and interprets data from published studies on the topic (in the scientific literature), then analyzes, describes, critically appraises and summarizes interpretations into a refined evidence-based ...

  9. External validity - Wikipedia

    en.wikipedia.org/wiki/External_validity

    In contrast, internal validity is the validity of conclusions drawn within the context of a particular study. Mathematical analysis of external validity concerns a determination of whether generalization across heterogeneous populations is feasible, and devising statistical and computational methods that produce valid generalizations. [4]