# GLM vs. t-tests vs. non-parametric tests if all we care about is NHST

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.

# Should the model-averaged prediction be computed on the link or response scale in a GLM?

[updated to include additional output from MuMIn, BMA, and BAS] This post is a follow up to my inital post, which was written as as a way for me to pen my mental thoughts on the recent review of “Model averaging in ecology: a review of Bayesian, information‐theoretic and tactical approaches for predictive inference”. It was also written without contacting and discussing the issue with the authors. This post benefits from a series of e-mails with the lead author Carsten Dormann and the last author Florian Hartig.

#### 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