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  2. Accuracy and precision - Wikipedia

    en.wikipedia.org/wiki/Accuracy_and_precision

    While precision is a description of random errors (a measure of statistical variability), accuracy has two different definitions: More commonly, a description of systematic errors (a measure of statistical bias of a given measure of central tendency, such as the mean). In this definition of "accuracy", the concept is independent of "precision ...

  3. Errors and residuals - Wikipedia

    en.wikipedia.org/wiki/Errors_and_residuals

    The statistical errors, on the other hand, are independent, and their sum within the random sample is almost surely not zero. One can standardize statistical errors (especially of a normal distribution) in a z-score (or "standard score"), and standardize residuals in a t-statistic, or more generally studentized residuals.

  4. Bias (statistics) - Wikipedia

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

    Detection bias occurs when a phenomenon is more likely to be observed for a particular set of study subjects. For instance, the syndemic involving obesity and diabetes may mean doctors are more likely to look for diabetes in obese patients than in thinner patients, leading to an inflation in diabetes among obese patients because of skewed detection efforts.

  5. Observational error - Wikipedia

    en.wikipedia.org/wiki/Observational_error

    When either randomness or uncertainty modeled by probability theory is attributed to such errors, they are "errors" in the sense in which that term is used in statistics; see errors and residuals in statistics. Every time a measurement is repeated, slightly different results are obtained.

  6. Calibration (statistics) - Wikipedia

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

    There are two main uses of the term calibration in statistics that denote special types of statistical inference problems. Calibration can mean a reverse process to regression, where instead of a future dependent variable being predicted from known explanatory variables, a known observation of the dependent variables is used to predict a corresponding explanatory variable; [1]

  7. Type I and type II errors - Wikipedia

    en.wikipedia.org/wiki/Type_I_and_type_II_errors

    In statistical hypothesis testing, a type I error, or a false positive, is the rejection of the null hypothesis when it is actually true. A type II error, or a false negative, is the failure to reject a null hypothesis that is actually false. [1] Type I error: an innocent person may be convicted. Type II error: a guilty person may be not convicted.

  8. Probability of error - Wikipedia

    en.wikipedia.org/wiki/Probability_of_error

    In statistics, the term "error" arises in two ways. ... This difference is known as an error, though when observed it would be better described as a residual.

  9. Error - Wikipedia

    en.wikipedia.org/wiki/Error

    In statistics, "error" refers to the difference between the ... An 'error' is a deviation from accuracy or correctness. ... by the difference (the error) between the ...