Getting over ANOVA: Estimation graphics for multi-group comparisons
Lu, Z.; Anns, J.; Mai, Y.; Zhang, R.; Lian, K.; Lee, N. M.; Hashir, S.; Wang Zhouyu, L.; Gonzalez, A. R. C.; Ho, J.; Choi, H.; Xu, S.; Claridge-Chang, A.
Show abstract
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.
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