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We began analyzing https://link.springer.com/article/10.1007/s10654-016-0149-3, but it redirected us to https://link.springer.com/article/10.1007/s10654-016-0149-3. The analysis below is for the second page.

Title[redir]:
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.

Matching Content Categories {📚}

  • Education
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Content Management System {📝}

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Custom-built

No common CMS systems were detected on Doi.org, and no known web development framework was identified.

Traffic Estimate {📈}

What is the average monthly size of doi.org audience?

🏙️ Massive Traffic: 50M - 100M visitors per month


Based on our best estimate, this website will receive around 98,426,998 visitors per month in the current month.

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How Does Doi.org Make Money? {💸}

The income method remains a mystery to us.

Some websites aren't about earning revenue; they're built to connect communities or raise awareness. There are numerous motivations behind creating websites. This might be one of them. Doi.org might be making money, but it's not detectable how they're doing it.

Keywords {🔍}

statistical, google, scholar, hypothesis, article, test, confidence, null, pubmed, data, values, intervals, effect, significance, tests, assumptions, hypotheses, model, cas, studies, power, statistics, results, testing, observed, interval, evidence, correct, study, probability, chance, analysis, size, difference, methods, research, true, small, significant, misinterpretations, large, alternative, compute, error, assumption, scientific, sizes, central, stat, epidemiology,

Topics {✒️}

uk/opinion/2114-journal-s-ban article download pdf lecture notes-monograph series placebo-controlled randomized trials null-hypothesis-significance-testing-reactions privacy choices/manage cookies federal judicial center loftus gr el-serag hb cox dr european economic area wash law rev seventh framework programme pre-study power calculations article greenland neyman stressed repeatedly mathematically related procedures lippincott-wolters-kluwer forking paths”—explains nat rev neurosci central role research triangle park tightly controlled experiments ann zool fenn controlled clinical trials charles poole central part crucial point lost basic biomedical disciplines related subjects multiple predictive factors psychon bull rev creative commons license work environ health environ health perspect traditional introductory expositions cost-effectiveness analysis main content log amorphous research settings null-hypothesis significance test chi squared statistic vote-counting methods testing additional terms provide lower bounds conventional statistical models full access providing sharp answers logically sound basis lash tl neglected historical debate

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 {🗺️}

WebPage:
      mainEntity:
         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.
         datePublished:2016-05-21T00:00:00Z
         dateModified:2016-05-21T00:00:00Z
         pageStart:337
         pageEnd:350
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            Hypothesis testing
            Null testing
             P value
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            Public Health
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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.
      datePublished:2016-05-21T00:00:00Z
      dateModified:2016-05-21T00:00:00Z
      pageStart:337
      pageEnd:350
      license:http://creativecommons.org/licenses/by/4.0/
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         Hypothesis testing
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         Infectious Diseases
         Cardiology
         Oncology
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            address:
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               type:PostalAddress
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               type: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

External Links {🔗}(389)

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