Bonferroni
Found 6 free book(s)Lecture 10: Multiple Testing - University of Washington
www.gs.washington.eduPhilosophical Objections to Bonferroni Corrections “Bonferroni adjustments are, at best, unnecessary and, at worst, deleterious to sound statistical inference” Perneger (1998) •Counter-intuitive: interpretation of finding depends on the number of other tests performed •The general null hypothesis (that all the null hypotheses are
1 Why is multiple testing a problem?
www.stat.berkeley.eduThe Bonferroni correction sets the signi cance cut-o at =n. For example, in the example above, with 20 tests and = 0:05, you’d only reject a null hypothesis if the p-value is less than 0.0025. The Bonferroni correction tends to be a bit too conservative. To demonstrate
Correlation Between Continuous & Categorical Variables
www.ce.memphis.eduBonferroni correction Observed Male Female Total Married 456 516 972 Widowed 58 123 181 Divorced 142 172 314 Separated 29 50 79 Never married 188 207 395 Total 873 1068 1941 Expected Male Female Married 437.1747 534.8253 Widowed 81.40804 99.59196 Divorced 141.2272 172.7728 Separated 35.53168 43.46832 Never married 177.6584 217.3416 Adjusted ...
Data Mining - Stanford University
infolab.stanford.eduof statistics, known as the Bonferroni correction gives a statistically sound way to avoid most of these bogus positive responses to a search through the data. Without going into the statistical details, we offer an informal version, Bon-ferroni’s principle, that helps us avoid treating random occurrences as if they were real.
Interaction Effects in ANOVA - University of Oregon
pages.uoregon.eduthis procedure for alpha adjustment is an application of Bonferroni’s procedure). Method 4. Planned Comparisons. Another alternative to the procedures above is the use of planned comparisons instead of the omnibus F test for the interaction. These procedures can also be implemented using the oneway procedures described above.
Model Selection: General Techniques - Stanford University
statweb.stanford.eduBonferroni correction Simultaneous inference for Model selection: goals Model selection: general Model selection: strategies Possible criteria Mallow’s Cp AIC & BIC Maximum likelihood estimation AIC for a linear model Search strategies Implementations in R Caveats - p. 3/16 Crude outlier detection test