# Reanalyzing data from Human Gut Microbiota from Autism Spectrum Disorder Promote Behavioral Symptoms in Mice

Update - This post has been updated A very skeletal analysis of Sharon, G., Cruz, N.J., Kang, D.W., Gandal, M.J., Wang, B., Kim, Y.M., Zink, E.M., Casey, C.P., Taylor, B.C., Lane, C.J. and Bramer, L.M., 2019. Human Gut Microbiota from Autism Spectrum Disorder Promote Behavioral Symptoms in Mice. Cell, 177(6), pp.1600-1618. 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 of if means and some kind of error bar is presented.

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

Update to the earlier post, which was written in response to my own thinking about how to teach stastics to experimental biologists working in fields that are dominated by hypothesis testing instead of estimation. That is, should these researchers learn GLMs or is a t-test on raw or log-transformed data on something like count data good enough – or even superior? My post was written without the benefit of either [Ives](Ives, Anthony R.

# Should we be skeptical of a "large" effect size if p > 0.05?

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

# Blocking vs. covariate adjustment

“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.

# The statistical significance filter

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.

# Covariate adjustment in randomized experiments

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?

# What to write, and not write, in a results section — an ever-growing list

“GPP (n=4 per site) increased from the No Wildlife site to the Hippo site but was lowest at the Hippo + WB site (Fig. 6); however, these differences were not significant due to low sample sizes and high variability.” – Subalusky, A.L., Dutton, C.L., Njoroge, L., Rosi, E.J., and Post, D.M. (2018). Organic matter and nutrient inputs from large wildlife influence ecosystem function in the Mara River, Africa. Ecology 99, 2558–2574.
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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