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Albert Cardona"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 ...
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