Search results
Results From The WOW.Com Content Network
With respect to FWER control, the Bonferroni correction can be conservative if there are a large number of tests and/or the test statistics are positively correlated. [9] Multiple-testing corrections, including the Bonferroni procedure, increase the probability of Type II errors when null hypotheses are false, i.e., they reduce statistical power.
The Holm–Bonferroni method is a shortcut procedure, since it makes or less comparisons, while the number of all intersections of null hypotheses to be tested is of order . It controls the FWER in the strong sense. In the Holm–Bonferroni procedure, we first test ().
Tukey’s Test (see also: Studentized Range Distribution) However, with the exception of Scheffès Method, these tests should be specified "a priori" despite being called "post-hoc" in conventional usage. For example, a difference between means could be significant with the Holm-Bonferroni method but not with the Turkey Test and vice versa.
The procedures of Bonferroni and Holm control the FWER under any dependence structure of the p-values (or equivalently the individual test statistics).Essentially, this is achieved by accommodating a `worst-case' dependence structure (which is close to independence for most practical purposes).
For example, for = 0.05 and m = 10, the Bonferroni-adjusted level is 0.005 and the Šidák-adjusted level is approximately 0.005116. One can also compute confidence intervals matching the test decision using the Šidák correction by computing each confidence interval at the (1 − α) 1/m % level.
Multiple comparisons arise when a statistical analysis involves multiple simultaneous statistical tests, each of which has a potential to produce a "discovery". A stated confidence level generally applies only to each test considered individually, but often it is desirable to have a confidence level for the whole family of simultaneous tests. [4]
In statistics, hypotheses suggested by a given dataset, when tested with the same dataset that suggested them, are likely to be accepted even when they are not true.This is because circular reasoning (double dipping) would be involved: something seems true in the limited data set; therefore we hypothesize that it is true in general; therefore we wrongly test it on the same, limited data set ...
The new multiple range test proposed by Duncan makes use of special protection levels based upon degrees of freedom. Let γ 2 , α = 1 − α {\displaystyle \gamma _{2,\alpha }={1-\alpha }} be the protection level for testing the significance of a difference between two means; that is, the probability that a significant difference between two ...