Contents
Why do p-values change in significance?
Why do p-values change in significance when changing the order of covariates in the aov model? I have a data set of 482 observations. Im going to do a genotype association analysis for 3 SNPs. Im trying to build a model for my analysis and Im using the aov (y~x,data=…).
How to calculate p values, confidence intervals, and power?
PMCID: PMC4877414 PMID: 27209009 Statistical tests, Pvalues, confidence intervals, and power: a guide to misinterpretations Sander Greenland,Stephen J. Senn,Kenneth J. Rothman,John B. Carlin,Charles Poole,Steven N. Goodman,and Douglas G. Altman Sander Greenland
If two predictor variables are correlated then the first one entered into the model will get full “credit,” leaving less variance to be “explained by” the second one, which thus may appear less “statistically significant” than the first even if it is not, functionally. This question and its answer explain the different Types of ANOVA analyses.
How does violation of often unstated analysis protocols lead to large pvalues?
We emphasize how violation of often unstated analysis protocols (such as selecting analyses for presentation based on the Pvalues they produce) can lead to small Pvalues even if the declared test hypothesis is correct, and can lead to large Pvalues even if that hypothesis is incorrect.
What’s the best way to adjust the p value?
The simplest way to adjust your P values is to use the conservative Bonferroni correction method which multiplies the raw P values by the number of tests m (i.e. length of the vector P_values). Using the p.adjust function and the ‘method’ argument set to “bonferroni”, we get a vector of same length but with adjusted P values.
Why are Bonferroni adjusted p-values not reported?
The reason for this is that probabilities cannot exceed 1. With respect to the previous example, this means that if an LSD p-value for one of the contrasts were .500, the Bonferroni-adjusted p-value reported would be 1.000 and not 1.500, which is the product of .5 multiplied by 3
Why are p values different for different comparisons?
The adjusted P value for one particular comparison would have a different value if there were a different number of comparisons or if the data in the other comparisons were changed. Each comparison will have a unique adjusted P value. But these P values are computed from all the comparisons, and really can’t be interpreted for just one comparison.