My name is David Colarusso. I founded and co-direct Suffolk University Law School's Legal Innovation & Technology (LIT) Lab. By training I'm an attorney & science educator. By experience, I'm a data s...

DABEST is available in both Python and R. The Python package, DABEST-python, is hosted on PyPI, and its source code can be found on GitHub at https://github.com/ACCLAB/DABEST-python. A tutorial for the Python package can be found at https://acclab.github.io/DABEST-python/tutorials/. The R variation, dabestr, is available on CRAN (https://cran.r-project.org/web/packages/dabestr) and similarly has its source code accessible on GitHub (https://github.com/ACCLAB/dabestr). A tutorial for the R package is available at https://acclab.github.io/dabestr/. Both repositories are released under the Apache-2.0 license. In addition, https://www.estimationstats.com provides an online user interface and performs all calculations using DABEST-Python. The Python notebook used to generate Fig. 1 and Supplementary Figs. 1–3 has been deposited to Zenodo (https://doi.org/10.5281/zenodo.18426513). This study was funded by grants from the Ministry of Education of Singapore (MOE): Z.L., R.Z., K.L., S.H., L.Z.W., Y.L., F.L. and A.R.C.G. were supported by 2022-MOET1-0001; J.A. was supported by FY2023-MOET1-0001; Y.M. was supported by a President’s Graduate Fellowship, MOE-T2EP30222-0018 (Research Scholarship) and MOE-T2EP30223-0009; N.M.L. was supported by Research Scholarships MOE2019-T2-1-133 and MOE-T2EP30222-0018; H.C. was supported in part by MOE (T2EP20223-0010) and the National Medical Research Council of Singapore (NMRC, CG21APR1008); S.X. was supported by the A*STAR Scientific Scholars Fund and NMRC (MOH-OFYIRG20nov-0051); A.C.-C. was supported by MOE (FY2022-MOET1-0001); A.C.-C. was also...

Nature Methods - Getting over ANOVA: estimation graphics for multi-group comparisons

Nature Methods - Getting over ANOVA: estimation graphics for multi-group comparisons

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. ### Competing Interest Statement The authors have declared no competing interest.