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  2. Kuder–Richardson formulas - Wikipedia

    en.wikipedia.org/wiki/Kuder–Richardson_formulas

    The name of this formula stems from the fact that is the twentieth formula discussed in Kuder and Richardson's seminal paper on test reliability. [1] It is a special case of Cronbach's α, computed for dichotomous scores. [2] [3] It is often claimed that a high KR-20 coefficient (e.g., > 0.90) indicates a homogeneous test. However, like ...

  3. Dixon's Q test - Wikipedia

    en.wikipedia.org/wiki/Dixon's_Q_test

    To apply a Q test for bad data, arrange the data in order of increasing values and calculate Q as defined: Q = gap range {\displaystyle Q={\frac {\text{gap}}{\text{range}}}} Where gap is the absolute difference between the outlier in question and the closest number to it.

  4. Sample size determination - Wikipedia

    en.wikipedia.org/wiki/Sample_size_determination

    The table shown on the right can be used in a two-sample t-test to estimate the sample sizes of an experimental group and a control group that are of equal size, that is, the total number of individuals in the trial is twice that of the number given, and the desired significance level is 0.05. [4] The parameters used are:

  5. Software reliability testing - Wikipedia

    en.wikipedia.org/wiki/Software_reliability_testing

    Software reliability is the probability that software will work properly in a specified environment and for a given amount of time. Using the following formula, the probability of failure is calculated by testing a sample of all available input states. Mean Time Between Failure(MTBF)=Mean Time To Failure(MTTF)+ Mean Time To Repair(MTTR)

  6. Regression validation - Wikipedia

    en.wikipedia.org/wiki/Regression_validation

    For example, if the functional form of the model does not match the data, R 2 can be high despite a poor model fit. Anscombe's quartet consists of four example data sets with similarly high R 2 values, but data that sometimes clearly does not fit the regression line. Instead, the data sets include outliers, high-leverage points, or non-linearities.

  7. Inter-rater reliability - Wikipedia

    en.wikipedia.org/wiki/Inter-rater_reliability

    However, the most accurate formula (which is applicable for all sample sizes) [14] is x ¯ ± t 0.05 , n − 1 s 1 + 1 n {\displaystyle {\bar {x}}\pm t_{0.05,n-1}s{\sqrt {1+{\frac {1}{n}}}}} Bland and Altman [ 15 ] have expanded on this idea by graphing the difference of each point, the mean difference, and the limits of agreement on the ...

  8. JASP - Wikipedia

    en.wikipedia.org/wiki/JASP

    Reliability: Quantify the reliability of test scores. Robust T-Tests : Robustly evaluate the difference between two means. SEM ( Structural equation modeling ) : Evaluate latent data structures with Yves Rosseel's lavaan program.

  9. Scott's Pi - Wikipedia

    en.wikipedia.org/wiki/Scott's_Pi

    Scott's pi (named after William A Scott) is a statistic for measuring inter-rater reliability for nominal data in communication studies.Textual entities are annotated with categories by different annotators, and various measures are used to assess the extent of agreement between the annotators, one of which is Scott's pi.