#statstab #499 SIMPLICITY AND COMPLEXITY IN ECOLOGICAL DATA ANALYSIS
Thoughts: I dislike this paper, but I don't mind sharing views that disagree with my own. Effective communication matters.
#analysis #critique #critical #ANOVA #ttest #simplicity #communication
https://doi.org/10.1890/0012-9658(2007)88[56:SACIED]2.0.CO;2
#statstab #485 Bayesian ANCOVA and the ATE
Thoughts: Still grappling with the implications of using the causal inference approach to randomized experiments. But it's interesting.
#ATE #causalinference #ancova #ANOVA #rstats #estimand #counterfactuals
#statstab #481 Getting over ANOVA: Estimation graphics for multi-group comparisons
Thoughts: Complex designs are harder to visualise, but with Estimation Statistics you get some perks over simple bar charts.
#design #estimationstatistics #ANOVA
https://www.biorxiv.org/content/10.64898/2026.01.26.701654v1
Due to a recent discussion with colleagues on whether and when to use #LinearMixedModels (#LMM), I wrote a blog post comparing LMM to other approaches using simulated data. I thought, it may also be useful for others working with hierarchical data structures in #neuroscience and beyond.
๐ https://www.fabriziomusacchio.com/blog/2026-01-31-linear_mixed_models/
#Python #Statistics #DataScience #MixedModels #Statsmodels #ANOVA #ANCOVA #GLMM #regression
In another network, senior author Adam Claridge-Chang publicly added this insightful and pungent commentary:
"Here's a dirty secret about ANOVA: it tests a null hypothesis that nobody cares about. When you run a one-way ANOVA, you're testing whether "all group means are equal." But even if you reject this hypothesis, you learn nothing about which groups differ, in which direction, or by how much. So you embark on a second analytical step: multiple two-group comparisons. A modest six-group experiment suddenly requires testing 15 hypotheses. To manage this multiplicity, you apply corrections like Bonferroni, which undermine your statistical power. What you posed as a focused research question has sprawled into a complex web of subsidiary tests, forced by the ANOVA ritual."
"Our new preprint, "Getting over ANOVA: Estimation graphics for multi-group comparisons," makes the case for a better approach. Estimation statistics encourages you to compare each test group to a single control, focusing on the effect sizes that actually matter. A six-group experiment focuses attention on just five effect sizes with confidence intervals, showing magnitude and precision directly."
"The preprint introduces estimation methods for a range of multi-group designs: repeated-measures experiments, 2ร2 factorial designs, binary outcome data, and mini-meta analysis for internal replicates. Each can replace data-analysis practices used in thousands of studies every year."
"Getting over ANOVA: Estimation graphics for multi-group comparisons", Lu et al. 2026 (Claridge-Chang's lab)
https://www.biorxiv.org/content/10.64898/2026.01.26.701654v1
"Data analysis in experimental science mainly relies on null-hypothesis significance testing, despite its well-known limitations. A powerful alternative is estimation statistics, which focuses on effect-size quantification. However, current estimation tools struggle with the complex, multi-group comparisons common in biological research. Here we introduce DABEST 2.0, an estimation framework for complex experimental designs, including shared-control, repeated-measures, two-way factorial experiments, and meta-analysis of replicates."
Grateful to Adam Claridge-Chang for leading and pushing on this. There's institutional-wide need for change in the biological sciences when it comes to statistical handling of data. And quite the memorable acronym, #DABEST ...
#statstab #462 Factorial Plots
Thoughts: Not the most modern of plots, but nice to have a guide on what to show based on your design.
#statstab #461 Interpreting Ordinal and Disordinal interactions
Thoughts: Interactions are not simple things. Their shape can determine many things (including sample size and effect size)
#design #ANOVA #interaction #effectsize #ordinal #crossover
https://www.jolley-mitchell.com/Appendix/WebAppOrdinalInteraction/WebAppOrdinalInteractions.htm
#statstab #460 {permuco} permutation tests in linear models with nuisances variables
Thoughts: Supports ANOVA, ANCOVA, t-tests and more.
#permutation #randomization #ANOVA #rstats #r #pvalues #ancova #ttest
Functions to compute p-values based on permutation tests. Regression, ANOVA and ANCOVA, omnibus F-tests, marginal unilateral and bilateral t-tests are available. Several methods to handle nuisance variables are implemented (Kherad-Pajouh, S., & Renaud, O. (2010) <doi:10.1016/j.csda.2010.02.015> ; Kherad-Pajouh, S., & Renaud, O. (2014) <doi:10.1007/s00362-014-0617-3> ; Winkler, A. M., Ridgway, G. R., Webster, M. A., Smith, S. M., & Nichols, T. E. (2014) <doi:10.1016/j.neuroimage.2014.01.060>). An extension for the comparison of signals issued from experimental conditions (e.g. EEG/ERP signals) is provided. Several corrections for multiple testing are possible, including the cluster-mass statistic (Maris, E., & Oostenveld, R. (2007) <doi:10.1016/j.jneumeth.2007.03.024>) and the threshold-free cluster enhancement (Smith, S. M., & Nichols, T. E. (2009) <doi:10.1016/j.neuroimage.2008.03.061>).
Dr Mircea Zloteanu ๐บ๐๐
Michael Westergaard
Fabrizio Musacchio
Albert Cardona