Why and How We Should Join the Shift From Significance Testing to Estimation Article Swipe
Daniel Berner
,
Valentin Amrhein
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202112.0235.v1
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202112.0235.v1
A paradigm shift away from null hypothesis significance testing seems in progress. Based on simulations, we illustrate some of the underlying motivations. First, P-values vary strongly from study to study, hence dichotomous inference using significance thresholds is usually unjustified. Second, statistically significant results have overestimated effect sizes, a bias declining with increasing statistical power. Third, statistically non-significant results have underestimated effect sizes, and this bias gets stronger with higher statistical power. Fourth, the tested statistical hypotheses generally lack biological justification and are often uninformative. Despite these problems, a screen of 48 papers from the 2020 volume of the Journal of Evolutionary Biology exemplifies that significance testing is still used almost universally in evolutionary biology. All screened studies tested the default null hypothesis of zero effect with the default significance threshold of p = 0.05, none presented a pre-planned alternative hypothesis, and none calculated statistical power and the probability of ‘false negatives’ (beta error). The papers reported 49 significance tests on average. Of 41 papers that contained verbal descriptions of a ‘statistically non-significant’ result, 26 (63%) falsely claimed the absence of an effect. We conclude that our studies in ecology and evolutionary biology are mostly exploratory and descriptive. We should thus shift from claiming to “test” specific hypotheses statistically to describing and discussing many hypotheses (effect sizes) that are most compatible with our data, given our statistical model. We already have the means for doing so, because we routinely present compatibility (“confidence”) intervals covering these hypotheses.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.20944/preprints202112.0235.v1
- https://www.preprints.org/manuscript/202112.0235/v1/download
- OA Status
- green
- Cited By
- 2
- References
- 84
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4200561891
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4200561891Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.20944/preprints202112.0235.v1Digital Object Identifier
- Title
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Why and How We Should Join the Shift From Significance Testing to EstimationWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-12-14Full publication date if available
- Authors
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Daniel Berner, Valentin AmrheinList of authors in order
- Landing page
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https://doi.org/10.20944/preprints202112.0235.v1Publisher landing page
- PDF URL
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https://www.preprints.org/manuscript/202112.0235/v1/downloadDirect link to full text PDF
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://www.preprints.org/manuscript/202112.0235/v1/downloadDirect OA link when available
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Null hypothesis, Statistical significance, Statistical hypothesis testing, Statistical power, Statistical inference, Inference, Null (SQL), Alternative hypothesis, Econometrics, Statistics, Type I and type II errors, Psychology, Mathematics, Computer science, Artificial intelligence, Data miningTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
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2023: 1, 2022: 1Per-year citation counts (last 5 years)
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84Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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