Traditional multiple-testing corrections, adjust p-values derived from multiple statistical tests to correct for the occurrence of false positives.
Topics covered include population sampling and generalizability, power and sample size calculations, correction for multiple statistical testing, and how to identify the appropriate use of statistics.
Multiple statistical tests indicated that these predictions were both unbiased and within the specified precision of the estimated regression equations.
For example, researchers may run multiple statistical tests on their data: they keep analysing the results in slightly different ways (known as "data mining") until they get a P-value less than 0.05.
Multiple statistical tests including the Ljung Box Q-test, Engle's test for heteroscedasticity, the Kolmogorov Smirnov test and the goodness of fit test were performed to show the validity of the proposed model.
Moreover, the question of multiple statistical testing is correctly addressed: "no adjustment was made for multiple comparisons".
Consequently, multiple statistical testing of several endpoints without or with only insufficient adjustment of the chosen significance level, or the use of inadequate statistical methods [21, 69 71].
We used a Bonferroni correction to account for the fact that we employed multiple statistical testing; a p value of 0.005 was used to signify statistical significance.
In the case of several primary endpoints, for an "A" rating, the problem of multiple statistical testing had to be discussed or considered accordingly in the statistical methodology.
Because of the multiple statistical testing resulting from the numerous variables measured, all results have to be interpreted in an exploratory rather than confirmatory manner.
