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Moving beyond P values: Everyday data analysis with estimation plots

View ORCID ProfileJoses Ho, View ORCID ProfileTayfun Tumkaya, View ORCID ProfileSameer Aryal, View ORCID ProfileHyungwon Choi, View ORCID ProfileAdam Claridge-Chang
doi: https://doi.org/10.1101/377978
Joses Ho
1Institute for Molecular and Cell Biology, A*STAR, Singapore 138673
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Tayfun Tumkaya
1Institute for Molecular and Cell Biology, A*STAR, Singapore 138673
2Department of Physiology, National University of Singapore, Singapore
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Sameer Aryal
1Institute for Molecular and Cell Biology, A*STAR, Singapore 138673
3Center for Neural Science, New York University, New York, NY, USA
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Hyungwon Choi
1Institute for Molecular and Cell Biology, A*STAR, Singapore 138673
4Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
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Adam Claridge-Chang
1Institute for Molecular and Cell Biology, A*STAR, Singapore 138673
2Department of Physiology, National University of Singapore, Singapore
5Program in Neuroscience and Behavioral Disorders, Duke-NUS Medical School, Singapore
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  • For correspondence: adamcc@gmail.com
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Abstract

Over the past 75 years, a number of statisticians have advised that the data-analysis method known as null-hypothesis significance testing (NHST) should be deprecated (Berkson, 1942; Halsey et al., 2015; Wasserstein et al., 2019). The limitations of NHST have been extensively discussed, with a broad consensus that current statistical practice in the biological sciences needs reform. However, there is less agreement on reform’s specific nature, with vigorous debate surrounding what would constitute a suitable alternative (Altman et al., 2000; Benjamin et al., 2017; Cumming and Calin-Jageman, 2016). An emerging view is that a more complete analytic technique would use statistical graphics to estimate effect sizes and evaluate their uncertainty (Cohen, 1994; Cumming and Calin-Jageman, 2016). As these estimation methods require only minimal statistical retraining, they have great potential to shift the current data-analysis culture away from dichotomous thinking towards quantitative reasoning (Claridge-Chang and Assam, 2016). The evolution of statistics has been inextricably linked to the development of quantitative displays that support complex visual reasoning (Tufte, 2001). We consider that the graphic we describe here as estimation plot is the most intuitive way to display the complete statistical information about experimental data sets. However, a major obstacle to adopting estimation plots is accessibility to suitable software. To lower this hurdle, we have developed free software that makes high-quality estimation plotting available to all. Here, we explain the rationale for estimation plots by contrasting them with conventional charts used to display data with NHST results, and describe how the use of these graphs affords five major analytical advantages.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted April 06, 2019.
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Moving beyond P values: Everyday data analysis with estimation plots
Joses Ho, Tayfun Tumkaya, Sameer Aryal, Hyungwon Choi, Adam Claridge-Chang
bioRxiv 377978; doi: https://doi.org/10.1101/377978
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Moving beyond P values: Everyday data analysis with estimation plots
Joses Ho, Tayfun Tumkaya, Sameer Aryal, Hyungwon Choi, Adam Claridge-Chang
bioRxiv 377978; doi: https://doi.org/10.1101/377978

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