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Lilliefors test is a normality test based on the Kolmogorov–Smirnov test. It is used to test the null hypothesis that data come from a normally distributed population, when the null hypothesis does not specify which normal distribution; i.e., it does not specify the expected value and variance of the distribution. [ 1 ]
The Bonferroni correction can also be applied as a p-value adjustment: Using that approach, instead of adjusting the alpha level, each p-value is multiplied by the number of tests (with adjusted p-values that exceed 1 then being reduced to 1), and the alpha level is left unchanged.
Although the 30 samples were all simulated under the null, one of the resulting p-values is small enough to produce a false rejection at the typical level 0.05 in the absence of correction. Multiple comparisons arise when a statistical analysis involves multiple simultaneous statistical tests, each of which has a potential to produce a "discovery".
Hubert Whitman Lilliefors (June 14, 1928 – February 23, 2008 in Bethesda, Maryland) was an American statistician, noted for his introduction of the Lilliefors test. Lilliefors received a BA in mathematics from George Washington University in 1952 [ 1 ] and his PhD at the George Washington University in 1964 under the supervision of Solomon ...
Partial autocorrelation function of Lake Huron's depth with confidence interval (in blue, plotted around 0). In time series analysis, the partial autocorrelation function (PACF) gives the partial correlation of a stationary time series with its own lagged values, regressed the values of the time series at all shorter lags.
Bartlett's test is used to test the null hypothesis, H 0 that all k population variances are equal against the alternative that at least two are different. If there are k samples with sizes and sample variances then Bartlett's test statistic is
ECMs are a theoretically-driven approach useful for estimating both short-term and long-term effects of one time series on another. The term error-correction relates to the fact that last-period's deviation from a long-run equilibrium, the error, influences its short-run dynamics. Thus ECMs directly estimate the speed at which a dependent ...
The Šidák correction is derived by assuming that the individual tests are independent. Let the significance threshold for each test be α 1 {\displaystyle \alpha _{1}} ; then the probability that at least one of the tests is significant under this threshold is (1 - the probability that none of them are significant).