#statstab #518 Before p < 0.05 to Beyond p < 0.05: Using History to
Contextualize p-Values and Significance Testing

Thoughts: An appropriate paper for today.

#pvalues #teaching #history #pedagogy #nhst #fisher

https://www.tandfonline.com/doi/pdf/10.1080/00031305.2018.1537891

#statstab #517 Generalizing Generalizability in Information Systems Research

Thoughts: Do your findings generalise? What does that mean?

#design #causalinference #theory #generalisability

https://pubsonline.informs.org/doi/10.1287/isre.14.3.221.16560

#statstab #516 Is there a boxplot variant for Poisson distributed data?

Thoughts: People like their canned visualisations, but you need to think what question you are answering.

#poisson #boxplot #sqrttransform #dataviz #r #outliers #distribution

https://stats.stackexchange.com/questions/13086/is-there-a-boxplot-variant-for-poisson-distributed-data

#statstab #515 Confounded dose-response effects of treatment adherence: fitting Bayesian instrumental variable models using brms

Thoughts: A nice tutorial on how to think about confounding, Simulating data, and making inferences

#brms #bayes #IV #guide

https://rpsychologist.com/adherence-analysis-IV-brms

#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/