#statstab #514 A puzzle of proportions

Thoughts: "Two popular Bayesian tests can yield dramatically different conclusions"
Model specification is important.

#bayesian #bayes #bayesfactor #nulleffects #proportions

https://doi.org/10.1002/sim.9278

#statstab #513 Some thoughts on checking the R session

Thoughts: Maybe we stop using rm(list=ls())? Setting up a good environment isn't always intuitive.

#rstats #r #coding #reproducibility #guide

https://blog.djnavarro.net/posts/2026-01-06_sessioncheck/

#statstab #512 Standardised mean difference estimators {shinyapp}

Thoughts: Calculating the correct SMD can be challenging, and most software are quite bad at it. Use this shiny app instead!

#cohend #SMD #hedgesg #heterogeneity #effectsize

https://effectsize.shinyapps.io/deffsize/

#statstab #511 Seven Myths of Randomisation
in Clinical Trials

Thoughts: Randomization is a very powerful tool for inference. Closest we have to magic in research. But it's also misunderstood.

#randomization #experiment #inference #design #bias #science

https://www.methodologyhubs.mrc.ac.uk/files/9214/3711/9501/Plenary-_Stephen_Senn.pdf

#statstab #510 A Note on Dropping Experimental Subjects who Fail a Manipulation Check

Thoughts: Another paper to consider when "removing participants who failed our manipulation check"🤷‍♂️

#manipulationcheck #estimand #experiment #design #bias #guide #assumptions #missingdata

https://doi.org/10.1017/pan.2019.5

#statstab #509 Effective sample size

Thoughts: ESS often reported for bayesian models, but is it really understood?

#brms #rstats #r #bayesian #ess #diagnostics

https://statmodeling.stat.columbia.edu/2025/11/27/effective-sample-size/

#statstab #508 Agreement Tests {SimplyAgree}

Thoughts: "R package was created to make the process of quantifying measurement agreement, consistency, and reliability"

#agreement #reliability #rstats #r #correlation #concordance

https://aaroncaldwell.us/SimplyAgree/articles/agree_tests.html

#statstab #507 Nonlinear models in {flocker}

Thoughts: I'm thinking these are useful if my theory predicts some natural limit (L) for a process. Like memory recall.

#nonlinear #glmm #brms #mem #asymtotic #flocker #r #rstats

https://www.maths.bris.ac.uk/R/web/packages/flocker/vignettes/nonlinear_models.html

#statstab #506 The VIF Score. What is it Good For? Absolutely Nothing

Thoughts: Maybe we need to think more closely about what these metrics mean.

#diagnostics #assumptions #regression #vif #variance #variableselection #error #multicolinearity

https://journals.sagepub.com/doi/abs/10.1177/10944281231216381

#statstab #505 Beyond Confounders

Thoughts: What makes a good control and a bad control?

#counterfactuals #confounder #DAG #r #modelling #selectionbias #variance #control #causalinference

https://matheusfacure.github.io/python-causality-handbook/07-Beyond-Confounders.html