If I'm going to evaluate the widespread use of t-tests/ANOVAs on count data in bench biology then I'd like to know what these data look like, specifically the shape (overdispersion) parameter.

A very skeletal analysis of 'Human Gut Microbiota from Autism Spectrum Disorder Promote Behavioral Symptoms in Mice', which got some attention on pubpeer. Commenters are questioning the result of Fig1G. It is very hard to infer a p-value from plots like these, where the data are multi-level, regardless if means and some kind of error bar is presented. A much better plot for inferring differences is an effects plot with the CI of the effect. That said, I'll try to reproduce the resulting p-value.

TL;DR -- If we live and die by NHST, then we want to choose a test with good Type I error control but has high power. The quasi-poisson both estimates an interpretable effect (unlike a t-test of log(y +1)) and has good Type I control with high power. A bit longer: The quasi-poisson LRT and the permutation NB have good Type I control and high power. The NB Wald and LRT have too liberal Type I control. The t-test of log response has good Type I control and high power at low $n$ but is slightly inferior to the glm with increased $n$. The t-test, Welch, and Wilcoxan have conservative Type I control. Of these, the Wilcoxan has higher power than the t-test and Welch but not as high as the GLMs or log-transformed response.

Motivator: A twitter comment “Isn’t the implication that the large effect size is a direct byproduct of the lack of power? i.e. that if the the study had more power, the effect size would have been found to be smaller.”1 2
A thought: our belief in the magnitude of an observed effect should be based on our priors, which, hopefully, are formed from good mechanistic models and not sample size“.3

“A more efficient design would be to first group the rats into homogeneous subsets based on baseline food consumption. This could be done by ranking the rats from heaviest to lightest eaters and then grouping them into pairs by taking the first two rats (the two that ate the most during baseline), then the next two in the list, and so on. The difference from a completely randomised design is that one rat within each pair is randomised to one of the treatment groups, and the other rat is then assigned to the remaining treatment group.

1 Why reported effect sizes are inflated 2 Setup 3 Exploration 1 4 Unconditional means, power, and sign error 5 Conditional means 5.1 filter = 0.05 5.2 filter = 0.2 1 Why reported effect sizes are inflated This post is motivated by many discussions in Gelman’s blog but start here
When we estimate an effect1, the estimate will be a little inflated or a little diminished relative to the true effect but the expectation of the effect is the true effect.

The post motivated by a tweetorial from Darren Dahly
In an experiment, do we adjust for covariates that differ between treatment levels measured pre-experiment (“imbalance” in random assignment), where a difference is inferred from a t-test with p < 0.05? Or do we adjust for all covariates, regardless of differences pre-test? Or do we adjust only for covariates that have sustantial correlation with the outcome? Or do we not adjust at all?