A skeleton simulation of different strategies for NHST for count data if all we care about is a p-value, as in bench biology where p-values are used to simply give one confidence that something didn’t go terribly wrong (similar to doing experiments in triplicate – it’s not the effect size that matters only “we have experimental evidence of a replicatable effect”) load libraries library(ggplot2) library(MASS) library(data.table) do_sim <- function(){ set.seed(1) niter <- 1000 methods <- c("t", "Welch", "log", "Wilcoxan", "nb") p_table <- matrix(NA, nrow=niter, ncol=length(methods)) colnames(p_table) <- methods res_table <- data.

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R doodles. Some ecology. Some physiology. Much fake data.

Thoughts on R, statistical best practices, and teaching applied statistics to Biology majors

Jeff Walker, Professor of Biological Sciences

University of Southern Maine, Portland, Maine, United States