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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. Accuracy and precision - Wikipedia

    en.wikipedia.org/wiki/Accuracy_and_precision

    Accuracy is also used as a statistical measure of how well a binary classification test correctly identifies or excludes a condition. That is, the accuracy is the proportion of correct predictions (both true positives and true negatives) among the total number of cases examined. [10]

  4. Statistical conclusion validity - Wikipedia

    en.wikipedia.org/.../Statistical_conclusion_validity

    Statistical conclusion validity is the degree to which conclusions about the relationship among variables based on the data are correct or "reasonable". This began as being solely about whether the statistical conclusion about the relationship of the variables was correct, but now there is a movement towards moving to "reasonable" conclusions that use: quantitative, statistical, and ...

  5. Response bias - Wikipedia

    en.wikipedia.org/wiki/Response_bias

    This is a distinct problem for self-report research because it does not allow a researcher to understand or gather accurate data from any type of question that asks for a participant to endorse or reject statements. [16] Researchers have approached this issue by thinking about the bias in two different ways.

  6. Member check - Wikipedia

    en.wikipedia.org/wiki/Member_check

    In qualitative research, a member check, also known as informant feedback or respondent validation, is a technique used by researchers to help improve the accuracy, credibility, validity, and transferability (also known as applicability, internal validity, [1] or fittingness) of a study. [2]

  7. Verification and validation of computer simulation models

    en.wikipedia.org/wiki/Verification_and...

    A model that has face validity appears to be a reasonable imitation of a real-world system to people who are knowledgeable of the real world system. [4] Face validity is tested by having users and people knowledgeable with the system examine model output for reasonableness and in the process identify deficiencies. [ 1 ]

  8. Reliability (statistics) - Wikipedia

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

    Reliability does not imply validity. That is, a reliable measure that is measuring something consistently is not necessarily measuring what is supposed to be measured. For example, while there are many reliable tests of specific abilities, not all of them would be valid for predicting, say, job performance.

  9. Data quality - Wikipedia

    en.wikipedia.org/wiki/Data_quality

    All data sourced from a third party to organization's internal teams may undergo accuracy (DQ) check against the third party data. These DQ check results are valuable when administered on data that made multiple hops after the point of entry of that data but before that data becomes authorized or stored for enterprise intelligence.