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Semantic and cognitive tools to aid statistical science: replace confidence and significance by compatibility and surprise

BACKGROUND: Researchers often misinterpret and misrepresent statistical outputs. This abuse has led to a large literature on modification or replacement of testing thresholds and P-values with confidence intervals, Bayes factors, and other devices. Because the core problems appear cognitive rather t...

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Autores principales: Rafi, Zad, Greenland, Sander
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7528258/
https://www.ncbi.nlm.nih.gov/pubmed/32998683
http://dx.doi.org/10.1186/s12874-020-01105-9
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author Rafi, Zad
Greenland, Sander
author_facet Rafi, Zad
Greenland, Sander
author_sort Rafi, Zad
collection PubMed
description BACKGROUND: Researchers often misinterpret and misrepresent statistical outputs. This abuse has led to a large literature on modification or replacement of testing thresholds and P-values with confidence intervals, Bayes factors, and other devices. Because the core problems appear cognitive rather than statistical, we review some simple methods to aid researchers in interpreting statistical outputs. These methods emphasize logical and information concepts over probability, and thus may be more robust to common misinterpretations than are traditional descriptions. METHODS: We use the Shannon transform of the P-value p, also known as the binary surprisal or S-value s = −log(2)(p), to provide a measure of the information supplied by the testing procedure, and to help calibrate intuitions against simple physical experiments like coin tossing. We also use tables or graphs of test statistics for alternative hypotheses, and interval estimates for different percentile levels, to thwart fallacies arising from arbitrary dichotomies. Finally, we reinterpret P-values and interval estimates in unconditional terms, which describe compatibility of data with the entire set of analysis assumptions. We illustrate these methods with a reanalysis of data from an existing record-based cohort study. CONCLUSIONS: In line with other recent recommendations, we advise that teaching materials and research reports discuss P-values as measures of compatibility rather than significance, compute P-values for alternative hypotheses whenever they are computed for null hypotheses, and interpret interval estimates as showing values of high compatibility with data, rather than regions of confidence. Our recommendations emphasize cognitive devices for displaying the compatibility of the observed data with various hypotheses of interest, rather than focusing on single hypothesis tests or interval estimates. We believe these simple reforms are well worth the minor effort they require.
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spelling pubmed-75282582020-10-01 Semantic and cognitive tools to aid statistical science: replace confidence and significance by compatibility and surprise Rafi, Zad Greenland, Sander BMC Med Res Methodol Technical Advance BACKGROUND: Researchers often misinterpret and misrepresent statistical outputs. This abuse has led to a large literature on modification or replacement of testing thresholds and P-values with confidence intervals, Bayes factors, and other devices. Because the core problems appear cognitive rather than statistical, we review some simple methods to aid researchers in interpreting statistical outputs. These methods emphasize logical and information concepts over probability, and thus may be more robust to common misinterpretations than are traditional descriptions. METHODS: We use the Shannon transform of the P-value p, also known as the binary surprisal or S-value s = −log(2)(p), to provide a measure of the information supplied by the testing procedure, and to help calibrate intuitions against simple physical experiments like coin tossing. We also use tables or graphs of test statistics for alternative hypotheses, and interval estimates for different percentile levels, to thwart fallacies arising from arbitrary dichotomies. Finally, we reinterpret P-values and interval estimates in unconditional terms, which describe compatibility of data with the entire set of analysis assumptions. We illustrate these methods with a reanalysis of data from an existing record-based cohort study. CONCLUSIONS: In line with other recent recommendations, we advise that teaching materials and research reports discuss P-values as measures of compatibility rather than significance, compute P-values for alternative hypotheses whenever they are computed for null hypotheses, and interpret interval estimates as showing values of high compatibility with data, rather than regions of confidence. Our recommendations emphasize cognitive devices for displaying the compatibility of the observed data with various hypotheses of interest, rather than focusing on single hypothesis tests or interval estimates. We believe these simple reforms are well worth the minor effort they require. BioMed Central 2020-09-30 /pmc/articles/PMC7528258/ /pubmed/32998683 http://dx.doi.org/10.1186/s12874-020-01105-9 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Technical Advance
Rafi, Zad
Greenland, Sander
Semantic and cognitive tools to aid statistical science: replace confidence and significance by compatibility and surprise
title Semantic and cognitive tools to aid statistical science: replace confidence and significance by compatibility and surprise
title_full Semantic and cognitive tools to aid statistical science: replace confidence and significance by compatibility and surprise
title_fullStr Semantic and cognitive tools to aid statistical science: replace confidence and significance by compatibility and surprise
title_full_unstemmed Semantic and cognitive tools to aid statistical science: replace confidence and significance by compatibility and surprise
title_short Semantic and cognitive tools to aid statistical science: replace confidence and significance by compatibility and surprise
title_sort semantic and cognitive tools to aid statistical science: replace confidence and significance by compatibility and surprise
topic Technical Advance
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7528258/
https://www.ncbi.nlm.nih.gov/pubmed/32998683
http://dx.doi.org/10.1186/s12874-020-01105-9
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