We are analyzing https://link.springer.com/article/10.1007/s10654-016-0149-3.

Title:
Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations | European Journal of Epidemiology
Description:
Misinterpretation and abuse of statistical tests, confidence intervals, and statistical power have been decried for decades, yet remain rampant. A key problem is that there are no interpretations of these concepts that are at once simple, intuitive, correct, and foolproof. Instead, correct use and interpretation of these statistics requires an attention to detail which seems to tax the patience of working scientists. This high cognitive demand has led to an epidemic of shortcut definitions and interpretations that are simply wrong, sometimes disastrously so—and yet these misinterpretations dominate much of the scientific literature. In light of this problem, we provide definitions and a discussion of basic statistics that are more general and critical than typically found in traditional introductory expositions. Our goal is to provide a resource for instructors, researchers, and consumers of statistics whose knowledge of statistical theory and technique may be limited but who wish to avoid and spot misinterpretations. We emphasize how violation of often unstated analysis protocols (such as selecting analyses for presentation based on the P values they produce) can lead to small P values even if the declared test hypothesis is correct, and can lead to large P values even if that hypothesis is incorrect. We then provide an explanatory list of 25 misinterpretations of P values, confidence intervals, and power. We conclude with guidelines for improving statistical interpretation and reporting.
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Keywords {🔍}

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Topics {✒️}

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Questions {❓}

Is proof of statistical significance relevant?
Low P-values or narrow confidence intervals: which are more durable?
Sifting the evidence—what’s wrong with significance tests?
The end of the P-value?
The environment and disease: association or causation?
Transparency and disclosure, neutrality and balance: shared values or just shared words?
Veto on the use of null hypothesis testing and p intervals: right or wrong?
What if there were no significance tests?

Schema {🗺️}

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         headline:Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations
         description:Misinterpretation and abuse of statistical tests, confidence intervals, and statistical power have been decried for decades, yet remain rampant. A key problem is that there are no interpretations of these concepts that are at once simple, intuitive, correct, and foolproof. Instead, correct use and interpretation of these statistics requires an attention to detail which seems to tax the patience of working scientists. This high cognitive demand has led to an epidemic of shortcut definitions and interpretations that are simply wrong, sometimes disastrously so—and yet these misinterpretations dominate much of the scientific literature. In light of this problem, we provide definitions and a discussion of basic statistics that are more general and critical than typically found in traditional introductory expositions. Our goal is to provide a resource for instructors, researchers, and consumers of statistics whose knowledge of statistical theory and technique may be limited but who wish to avoid and spot misinterpretations. We emphasize how violation of often unstated analysis protocols (such as selecting analyses for presentation based on the P values they produce) can lead to small P values even if the declared test hypothesis is correct, and can lead to large P values even if that hypothesis is incorrect. We then provide an explanatory list of 25 misinterpretations of P values, confidence intervals, and power. We conclude with guidelines for improving statistical interpretation and reporting.
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            Oncology
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      headline:Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations
      description:Misinterpretation and abuse of statistical tests, confidence intervals, and statistical power have been decried for decades, yet remain rampant. A key problem is that there are no interpretations of these concepts that are at once simple, intuitive, correct, and foolproof. Instead, correct use and interpretation of these statistics requires an attention to detail which seems to tax the patience of working scientists. This high cognitive demand has led to an epidemic of shortcut definitions and interpretations that are simply wrong, sometimes disastrously so—and yet these misinterpretations dominate much of the scientific literature. In light of this problem, we provide definitions and a discussion of basic statistics that are more general and critical than typically found in traditional introductory expositions. Our goal is to provide a resource for instructors, researchers, and consumers of statistics whose knowledge of statistical theory and technique may be limited but who wish to avoid and spot misinterpretations. We emphasize how violation of often unstated analysis protocols (such as selecting analyses for presentation based on the P values they produce) can lead to small P values even if the declared test hypothesis is correct, and can lead to large P values even if that hypothesis is incorrect. We then provide an explanatory list of 25 misinterpretations of P values, confidence intervals, and power. We conclude with guidelines for improving statistical interpretation and reporting.
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         Hypothesis testing
         Null testing
          P value
         Power
         Significance tests
         Statistical testing
         Epidemiology
         Public Health
         Infectious Diseases
         Cardiology
         Oncology
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         type:PostalAddress
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      email:[email protected]
      name:Stephen J. Senn
      affiliation:
            name:Luxembourg Institute of Health
            address:
               name:Competence Center for Methodology and Statistics, Luxembourg Institute of Health, Strassen, Luxembourg
               type:PostalAddress
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      affiliation:
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            address:
               name:Clinical Epidemiology and Biostatistics Unit, Murdoch Children’s Research Institute, School of Population Health, University of Melbourne, Melbourne, Australia
               type:PostalAddress
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      affiliation:
            name:University of North Carolina
            address:
               name:Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, USA
               type:PostalAddress
            type:Organization
      name:Steven N. Goodman
      affiliation:
            name:Stanford University School of Medicine
            address:
               name:Meta-Research Innovation Center, Departments of Medicine and of Health Research and Policy, Stanford University School of Medicine, Stanford, USA
               type:PostalAddress
            type:Organization
      name:Douglas G. Altman
      affiliation:
            name:University of Oxford
            address:
               name:Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
               type:PostalAddress
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PostalAddress:
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      name:Competence Center for Methodology and Statistics, Luxembourg Institute of Health, Strassen, Luxembourg
      name:RTI Health Solutions, Research Triangle Institute, Research Triangle Park, USA
      name:Clinical Epidemiology and Biostatistics Unit, Murdoch Children’s Research Institute, School of Population Health, University of Melbourne, Melbourne, Australia
      name:Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, USA
      name:Meta-Research Innovation Center, Departments of Medicine and of Health Research and Policy, Stanford University School of Medicine, Stanford, USA
      name:Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK

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