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Moving beyond P values: data analysis with estimation graphics

Nature Methods volume 16, pages 565–566 (2019)Cite this article

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To the Editor — For at least 77 years, the limitations of null-hypothesis significance testing (NHST) have been discussed, without agreement on a suitable alternative1,2. Estimation methods that estimate effect sizes and their uncertainty have great potential to shift the current data-analysis culture away from dichotomous thinking and toward quantitative reasoning3,4. Although NHST limits the analyst to the ill-conceived question of “Does it?”5, estimation instead draws the analyst’s attention to the question of “How much?”—the very topic that defines quantitative research. Here we describe the estimation graphic, a plot that displays an experimental dataset’s complete statistical information. We also introduce software that makes high-quality estimation graphics available to all.

Although visualizing the null distribution is an improvement, this picture nevertheless illustrates NHST’s flawed logic: to ‘prove’ that the null hypothesis is false, the analyst must invoke the existence of something (the tail segment) that the hypothesis itself predicts1. Even the premise of NHST is unrealistic: any intervention to a system will produce some (at least infinitesimal) effect; thus a hypothesized effect size of precisely zero is inevitably false3.

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Fig. 1: The evolution of two-group data graphics.

Data availability

The Matlab, Python and R packages are available on GitHub (https://github.com/ACCLAB/DABEST-python) and are licensed under the BSD 3-Clause Clear License.

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Acknowledgements

We thank M. Rahman for help and advice, and H. Nguyen for developing the web app front end.

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Authors and Affiliations

  1. Institute for Molecular and Cell Biology, A*STAR, Singapore, Singapore

    Joses Ho, Tayfun Tumkaya, Sameer Aryal, Hyungwon Choi & Adam Claridge-Chang

  2. Department of Physiology, National University of Singapore, Singapore, Singapore

    Tayfun Tumkaya & Adam Claridge-Chang

  3. Center for Neural Science, New York University, New York, NY, USA

    Sameer Aryal

  4. Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

    Hyungwon Choi

  5. Program in Neuroscience and Behavioral Disorders, Duke-NUS Medical School, Singapore, Singapore

    Adam Claridge-Chang

Authors
  1. Joses Ho
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  2. Tayfun Tumkaya
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  3. Sameer Aryal
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  4. Hyungwon Choi
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  5. Adam Claridge-Chang
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Corresponding author

Correspondence to Adam Claridge-Chang.

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The authors declare no competing interests.

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Supplementary Notes 1–4 and Supplementary Figs. 1 and 2.

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Ho, J., Tumkaya, T., Aryal, S. et al. Moving beyond P values: data analysis with estimation graphics. Nat Methods 16, 565–566 (2019). https://doi.org/10.1038/s41592-019-0470-3

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