The DOI matches its Crossref record; 130 of the 135 references it cites were found in a registry or an index, and 1 are not checked yet.
Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Major threats that have high prevalence include the misuse and misinterpretation of null-hypothesis testing methods19,20”
From How to design preclinical studies in nanomedicine and cell therapy to maximize the prospects of clinical translation (2018) · cites this paper as [20]
In the citing paper
“The use of statistical methods has been demonstrated to be suboptimal in some fields.”
From How to design preclinical studies in nanomedicine and cell therapy to maximize the prospects of clinical translation (2018) · cites this paper as [20]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009) · doi:10.1002/9780470743386 · Cited by 14,225
Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2021) · doi:10.1002/9781119558378 · Cited by 9,799
The environment and disease: association or causation?
Hill, A. B. (2015) · Journal of the Royal Society of Medicine · doi:10.1177/0141076814562718 · Cited by 7,804
Power failure: why small sample size undermines the reliability of neuroscience
Button, K. S., Ioannidis, J. P. A., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S. J., & Munafò, M. R. (2013) · Nature Reviews Neuroscience · doi:10.1038/nrn3475 · Cited by 7,641
Sontrop, J. M., Dixon, S. N., Garg, A. X., Jiménez, I. B., Dohein, O., Huang, S. S., & Clark, W. F. (2013) · American Journal of Nephrology · doi:10.1159/000350377 · Cited by 5,903
Methods of Meta-Analysis: Correcting Error and Bias in Research Findings
Schmidt, F. L., & Hunter, J. E. (2015) · doi:10.4135/9781483398105 · Cited by 5,542
The Handbook of Research Synthesis and Meta-Analysis
(2019) · doi:10.7758/9781610448864 · Cited by 4,553
Cohen, J. (1994) · American Psychologist · doi:10.1037/0003-066x.49.12.997 · Cited by 4,003
Cox, D. R., & Hinkley, D. V. (1974) · doi:10.1007/978-1-4899-2887-0 · Cited by 3,089
A practical solution to the pervasive problems ofp values
Wagenmakers, E.-J. (2007) · Psychonomic Bulletin & Review · doi:10.3758/bf03194105 · Cited by 2,490
Methods of Meta-Analysis: Correcting Error and Bias in Research Findings.
Raudenbush, S. W., Hunter, J. E., & Schmidt, F. L. (1991) · Journal of the American Statistical Association · doi:10.2307/2289738 · Cited by 2,368
Rothman, K. J., Greenland, S., & Lash, T. L. (1998) · Cited by 2,096
Self-citationThe Abuse of Power: The Pervasive Fallacy of Power Calculations for Data Analysis
Hoenig, J. M., & Heisey, D. M. (2001) · The American Statistician · doi:10.1198/000313001300339897 · Cited by 2,046
(2011) · Cited by 2,025
Confidence intervals rather than P values: estimation rather than hypothesis testing.
Gardner, M. J., & Altman, D. G. (1986) · BMJ · doi:10.1136/bmj.292.6522.746 · Cited by 2,014
Self-citationStatistics notes: Absence of evidence is not evidence of absence
Altman, D. G., & Bland, J. M. (1995) · BMJ · doi:10.1136/bmj.311.7003.485 · Cited by 1,946
Self-citationTesting Statistical Hypotheses
Lehmann, E. L. (1986) · Springer texts in statistics · doi:10.1007/978-1-4757-1923-9 · Cited by 1,876
Smithson, M. J. (2000) · SAGE Publications Ltd eBooks · doi:10.4135/9781446218525 · Cited by 1,871
Statistical methods and scientific inference.
Snell, J., & Fisher, R. (1957) · Journal of the Royal Statistical Society Series A (General) · doi:10.2307/2342557 · Cited by 1,819
Bayesian statistical inference for psychological research.
Edwards, W., Lindman, H., & Savage, L. J. (1963) · Psychological Review · doi:10.1037/h0044139 · Cited by 1,734
Sterne, J. A. C. (2001) · BMJ · doi:10.1136/bmj.322.7280.226 · Cited by 1,466
The Difference Between “Significant” and “Not Significant” is not Itself Statistically Significant
Gelman, A., & Stern, H. (2006) · The American Statistician · doi:10.1198/000313006x152649 · Cited by 1,407 · cited 1×
“…and Boen 1972; Berkson 1942; Bland and Altman 2015; Chia 1997; Cohen 1994; Evans et al. 1988; Fidler and Loftus 2009; Gardner and Altman 1986; Gelman 2013; Gelman and Loken 2014; Gelman and Stern 2006; Gigerenzer 2004; Gigerenzer and Marewski 2015; Goodman 1992, 1993, 1999, 2008; Greenland 2011,”
BackgroundToward Evidence-Based Medical Statistics. 1: The P Value Fallacy
Goodman, S. N. (1999) · Annals of Internal Medicine · doi:10.7326/0003-4819-130-12-199906150-00008 · Cited by 1,188
Self-citationON THE USE AND INTERPRETATION OF CERTAIN TEST CRITERIA FOR PURPOSES OF STATISTICAL INFERENCE
Neyman, J., & Pearson, E. S. (1928) · Biometrika · doi:10.1093/biomet/20a.3-4.263 · Cited by 1,164
Dwan, K., Gamble, C., Williamson, P. R., Kirkham, J. J., & the Reporting Bias Group (2013) · PLoS ONE · doi:10.1371/journal.pone.0066844 · Cited by 1,127
Outline of a Theory of Statistical Estimation Based on the Classical Theory of Probability
Neyman, J. (1937) · Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences · doi:10.1098/rsta.1937.0005 · Cited by 1,087
Schmidt, F. L. (1996) · Psychological Methods · doi:10.1037/1082-989x.1.2.115 · Cited by 1,044
Testing a Point Null Hypothesis: The Irreconcilability of P Values and Evidence
Berger, J. O., & Sellke, T. (1987) · Journal of the American Statistical Association · doi:10.1080/01621459.1987.10478397 · Cited by 985
Testing Statistical Hypotheses.
Biggins, J. D., & Lehmann, E. L. (1988) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.2307/2982206 · Cited by 975
The Statistical Crisis in Science
Gelman, A., & Loken, E. (2014) · American Scientist · doi:10.1511/2014.111.460 · Cited by 963
The test of significance in psychological research.
Bakan, D. (1966) · Psychological Bulletin · doi:10.1037/h0020412 · Cited by 957
Petitti, D. B. (1994) · Medical Entomology and Zoology · Cited by 869
Statistical procedures and the justification of knowledge in psychological science.
Rosnow, R. L., & Rosenthal, R. (1989) · American Psychologist · doi:10.1037/0003-066x.44.10.1276 · Cited by 863
Calibration of ρ Values for Testing Precise Null Hypotheses
Sellke, T., Bayarri, M. J., & Berger, J. O. (2001) · The American Statistician · doi:10.1198/000313001300339950 · Cited by 856
Meta‐Analysis Of Controlled Clinical Trials
Whitehead, A. (2002) · doi:10.1002/0470854200 · Cited by 827
The fallacy of the null-hypothesis significance test.
Rozeboom, W. W. (1960) · Psychological Bulletin · doi:10.1037/h0042040 · Cited by 740
Statistical Methods and Scientific Inference.
Jowett, G. H., & Fisher, R. A. (1957) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.2307/2985611 · Cited by 717
The cult of statistical significance: how the standard error costs us jobs, justice, and lives
(2008) · Choice Reviews Online · doi:10.5860/choice.46-0372 · Cited by 716
A dirty dozen: twelve p-value misconceptions.
Goodman, S. (2008) · Seminars in hematology (Print) · doi:10.1053/j.seminhematol.2008.04.003 · Cited by 694
Self-citationWhat If There Were No Significance Tests?
(2013) · Psychology Press eBooks · doi:10.4324/9781315827353 · Cited by 675
Statistical Problems in the Reporting of Clinical Trials
Pocock, S., Hughes, M. D., & Lee, R. J. (1987) · New England Journal of Medicine · doi:10.1056/nejm198708133170706 · Cited by 663
Goodman, S. N., & Berlin, J. A. (1994) · Annals of Internal Medicine · doi:10.7326/0003-4819-121-3-199408010-00008 · Cited by 658
Self-citationStatistical procedures and the justification of knowledge in psychological science.
Rosnow, R. L., & Rosenthal, R. (1992) · American Psychological Association eBooks · doi:10.1037/10109-027 · Cited by 653
Good practices for quantitative bias analysis
Lash, T. L., Fox, M. P., MacLehose, R. F., Maldonado, G., McCandless, L. C., & Greenland, S. (2014) · International Journal of Epidemiology · doi:10.1093/ije/dyu149 · Cited by 651
Self-citationTrafimow, D., & Marks, M. (2015) · Basic and Applied Social Psychology · doi:10.1080/01973533.2015.1012991 · Cited by 618
The Empire of chance: how probability changed science and everyday life
(1989) · Choice Reviews Online · doi:10.5860/choice.27-2084 · Cited by 600
Eyding, D., Lelgemann, M., Grouven, U., Härter, M. C., Kromp, M., Kaiser, T., Kerekeş, M., Gerken, M., & Wieseler, B. (2010) · BMJ · doi:10.1136/bmj.c4737 · Cited by 600
The fallacy of placing confidence in confidence intervals
Morey, R. D., Hoekstra, R., Rouder, J. N., Lee, M. D., & Wagenmakers, E.-J. (2016) · Psychonomic Bulletin & Review · doi:10.3758/s13423-015-0947-8 · Cited by 577
Xu, L., Freeman, G., Cowling, B. J., & Schooling, C. M. (2013) · BMC Medicine · doi:10.1186/1741-7015-11-108 · Cited by 569
Testing a Point Null Hypothesis: The Irreconcilability of P Values and Evidence
Berger, J. O., & Sellke, T. (1987) · Journal of the American Statistical Association · doi:10.2307/2289131 · Cited by 548
Vote-counting methods in research synthesis.
Hedges, L., & Olkin, I. (1980) · Psychological Bulletin · doi:10.1037/0033-2909.88.2.359 · Cited by 511
The Significance Test Controversy.
Carter, L. F., Morrison, D. E., & Henkel, R. E. (1971) · Social Forces · doi:10.2307/2576949 · Cited by 443
Reconciling Bayesian and Frequentist Evidence in the One-Sided Testing Problem
Casella, G., & Berger, R. L. (1987) · Journal of the American Statistical Association · doi:10.1080/01621459.1987.10478396 · Cited by 417
Early childhood intervention: A promising preventative for juvenile delinquency.
Zigler, E., Taussig, C., & BLACK, K. N. (1992) · American Psychologist · doi:10.1037//0003-066x.47.8.997 · Cited by 390
Statistical Inference: A Commentary for the Social and Behavioural Sciences
Oakes, M. W. (1986) · Medical Entomology and Zoology · Cited by 388
Randomization, Statistics, and Causal Inference
Greenland, S. (1990) · Epidemiology · doi:10.1097/00001648-199011000-00003 · Cited by 385
Self-citationLow P-Values or Narrow Confidence Intervals: Which Are More Durable?
Poole, C. (2001) · Epidemiology · doi:10.1097/00001648-200105000-00005 · Cited by 363
Self-citationPsychology Will Be a Much Better Science When We Change the Way We Analyze Data
Loftus, G. R. (1996) · Current Directions in Psychological Science · doi:10.1111/1467-8721.ep11512376 · Cited by 362
Edwards, A. W. F. (1992) · Johns Hopkins University Press eBooks · doi:10.56021/9780801844454 · Cited by 358
Drinking Water Source and Chlorination Byproducts II. Risk of Colon and Rectal Cancers
Hildesheim, M. E., Cantor, K. P., Lynch, C. F., Dosemeci, M., Lubin, J. H., Alavanja, M. C. R., & Craun, G. F. (1998) · Epidemiology · doi:10.1097/00001648-199801000-00008 · Cited by 357
Statistical significance tests.
Cox, D. (1982) · British Journal of Clinical Pharmacology · doi:10.1111/j.1365-2125.1982.tb01987.x · Cited by 335
Murtaugh, P. A. (2014) · Ecology · doi:10.1890/13-0590.1 · Cited by 315
A comment on replication,P‐values and evidence
Goodman, S. N. (1992) · Statistics in Medicine · doi:10.1002/sim.4780110705 · Cited by 309
Self-citationGoodman, S. N. (1993) · American Journal of Epidemiology · doi:10.1093/oxfordjournals.aje.a116700 · Cited by 306
Self-citationFinal Collapse of the Neyman-Pearson Decision Theoretic Framework and Rise of the neoFisherian
Hurlbert, S. H., & Lombardi, C. M. (2009) · Annales Zoologici Fennici · doi:10.5735/086.046.0501 · Cited by 305
Rothman, K. J. (1978) · New England Journal of Medicine · doi:10.1056/nejm197812142992410 · Cited by 281
Self-citationPage, M. J., McKenzie, J. E., Kirkham, J., Dwan, K., Kramer, S., Green, S., & Forbes, A. (2014) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.mr000035.pub2 · Cited by 281
Severe Testing as a Basic Concept in a Neyman–Pearson Philosophy of Induction
Mayo, D. G., & Spanos, A. (2006) · The British Journal for the Philosophy of Science · doi:10.1093/bjps/axl003 · Cited by 271
META-ANALYSIS IN THE DESIGN AND MONITORING OF CLINICAL TRIALS
DerSimonian, R. (1996) · Statistics in Medicine · doi:10.1002/(sici)1097-0258(19960630)15:12 · Cited by 257
Estimating Cancer Risks from Low Doses of Ionizing Radiation
Land, C. E. (1980) · Science · doi:10.1126/science.7403879 · Cited by 250
Dirty Dozen: Twelve P-Value Misconceptions
Goodman, T. (2008) · Cited by 238
Beyond the confidence interval.
Poole, C. (1987) · American Journal of Public Health · doi:10.2105/ajph.77.2.195 · Cited by 229
Self-citationGelman, A. (2012) · Epidemiology · doi:10.1097/ede.0b013e31827886f7 · Cited by 228
Evidence and scientific research.
Goodman, S. N., & Royall, R. M. (1988) · American Journal of Public Health · doi:10.2105/ajph.78.12.1568 · Cited by 227
Self-citationGigerenzer, G., & Marewski, J. N. (2014) · Journal of Management · doi:10.1177/0149206314547522 · Cited by 227
Tests of Significance Considered as Evidence
Berkson, J. (1942) · Journal of the American Statistical Association · doi:10.1080/01621459.1942.10501760 · Cited by 203
The Religion of Statistics as Practiced in Medical Journals
Salsburg, D. S. (1985) · The American Statistician · doi:10.1080/00031305.1985.10479435 · Cited by 194
Bayesian Interpretation of Standard Inference Statements
Pratt, J. W. (1965) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1965.tb01486.x · Cited by 176
Tests of Significance Considered as Evidence
Berkson, J. (1942) · Journal of the American Statistical Association · doi:10.2307/2279000 · Cited by 174
Statistics Notes: Interaction 2: compare effect sizes not P values
Matthews, J. N. S., & Altman, D. G. (1996) · BMJ · doi:10.1136/bmj.313.7060.808 · Cited by 174
Self-citationThe Counternull Value of an Effect Size: A New Statistic
Rosenthal, R., & Rubin, D. B. (1994) · Psychological Science · doi:10.1111/j.1467-9280.1994.tb00281.x · Cited by 170
PRODAN, M. (1968) · Elsevier eBooks · doi:10.1016/b978-0-08-012441-4.50022-5 · Cited by 164
Introduction to Bayesian methods I: measuring the strength of evidence
Goodman, S. N. (2005) · Clinical Trials · doi:10.1191/1740774505cn098oa · Cited by 158
Self-citationThe Empire of Chance: How Probability Changed Science and Everyday Life.
Guttorp, P. M., Gigerenzer, G., Swijtink, Z. G., Porter, T. M., Daston, L., Beatty, J., & Krüger, L. (1990) · Journal of the American Statistical Association · doi:10.2307/2289806 · Cited by 151
Rothman, K. J. (1986) · Annals of Internal Medicine · doi:10.7326/0003-4819-105-3-445 · Cited by 144
Self-citationSignificance tests as sorcery: Science is empirical—significance tests are not
Lambdin, C. (2012) · Theory & Psychology · doi:10.1177/0959354311429854 · Cited by 143
Bland, M., & Altman, D. G. (2015) · American Journal of Clinical Nutrition · doi:10.3945/ajcn.115.119768 · Cited by 136
Self-citationSmith, A. H., & Bates, M. N. (1992) · Epidemiology · doi:10.1097/00001648-199209000-00011 · Cited by 128
Koff, R. S. (1995) · Hepatology · doi:10.1002/hep.1840210146 · Cited by 126
Wood, J., Freemantle, N., King, M., & Nazareth, I. (2014) · BMJ · doi:10.1136/bmj.g2215 · Cited by 125
Oral contraception and the risk of hepatocellular carcinoma
Maheshwari, S., Sarraj, A., Kramer, J., & El‐Serag, H. B. (2007) · Journal of Hepatology · doi:10.1016/j.jhep.2007.03.015 · Cited by 124
Frequentist statistics as a theory of inductive inference
Mayo, D. G., & Cox, D. (2006) · Institute of Mathematical Statistics eBooks · doi:10.1214/074921706000000400 · Cited by 123
Demystifying Social Statistics
Lyon, E. S., Irvine, J. M., Miles, I. D., & Evans, J. (1980) · British Journal of Sociology · doi:10.2307/589386 · Cited by 120
Yates, F. (1951) · Journal of the American Statistical Association · doi:10.1080/01621459.1951.10500764 · Cited by 118
Greenland, S., & Poole, C. (2012) · Epidemiology · doi:10.1097/ede.0b013e3182785741 · Cited by 115
Self-citationProbability as certainty: Dichotomous thinking and the misuse ofp values
Hoekstra, R., Finch, S., Kiers, H., & Johnson, A. M. (2006) · Psychonomic Bulletin & Review · doi:10.3758/bf03213921 · Cited by 113
Null misinterpretation in statistical testing and its impact on health risk assessment
Greenland, S. (2011) · Preventive Medicine · doi:10.1016/j.ypmed.2011.08.010 · Cited by 99
Self-citationSenn, S. J. (2001) · Journal of Epidemiology and Biostatistics · doi:10.1080/135952201753172953 · Cited by 98
Self-citationNonsignificance Plus High Power Does Not Imply Support for the Null Over the Alternative
Greenland, S. (2012) · Annals of Epidemiology · doi:10.1016/j.annepidem.2012.02.007 · Cited by 97
Self-citationReporting the results of epidemiologic studies.
Walker, A. M. (1986) · American Journal of Public Health · doi:10.2105/ajph.76.5.556 · Cited by 93
Statistical Concepts in Their Relation to Reality
Pearson, E. S. (1955) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1955.tb00194.x · Cited by 92
Chen, D., & Peace, K. E. (2013) · Chapman & Hall/CRC eBooks · doi:10.1201/b14872 · Cited by 89
Even statisticians are not immune to misinterpretations of Null Hypothesis Significance Tests
Lecoutre, M., Poitevineau, J., & Lecoutre, B. (2003) · International Journal of Psychology · doi:10.1080/00207590244000250 · Cited by 86
A comment on replication, p‐values and evidence S.N.Goodman, Statistics in Medicine 1992; 11:875‐879
Senn, S. J. (2002) · Statistics in Medicine · doi:10.1002/sim.1072 · Cited by 84
Self-citationStatistical criteria in the interpretation of epidemiologic data.
Thompson, W. (1987) · American Journal of Public Health · doi:10.2105/ajph.77.2.191 · Cited by 79
Why Figures with Error Bars Should Replace p Values
Fidler, F. M., & Loftus, G. R. (2009) · Zeitschrift für Psychologie · doi:10.1027/0044-3409.217.1.27 · Cited by 77
You, B., Gan, H. K., Pond, G. R., & Chen, E. X. (2011) · Journal of Clinical Oncology · doi:10.1200/jco.2011.37.0890 · Cited by 74
Towards estimation and confidence intervals.
LANGMAN, M. J. S. (1986) · BMJ · doi:10.1136/bmj.292.6522.716 · Cited by 73
Translating statistical findings into plain English
Pocock, S. J., & Ware, J. H. (2009) · The Lancet · doi:10.1016/s0140-6736(09)60499-2 · Cited by 67
It’s Time to Rehabilitate the P-Value
Weinberg, C. R. (2001) · Epidemiology · doi:10.1097/00001648-200105000-00004 · Cited by 66
Power is indeed irrelevant in interpreting completed studies
Senn, S. J. (2002) · BMJ · doi:10.1136/bmj.325.7375.1304 · Cited by 58
Self-citationEvans, S. J. W., Mills, P. A., & Dawson, J. (1988) · Heart · doi:10.1136/hrt.60.3.177 · Cited by 57
Lew, M. J. (2012) · British Journal of Pharmacology · doi:10.1111/j.1476-5381.2012.01931.x · Cited by 55
Translating statistical findings into plain English
Stang, A. (2009) · The Lancet · doi:10.1016/s0140-6736(09)61703-7 · Cited by 48
Confidence intervals exclude nothing.
Poole, C. (1987) · American Journal of Public Health · doi:10.2105/ajph.77.4.492 · Cited by 46
Self-citationChia, K. (1997) · Scandinavian Journal of Work Environment & Health · doi:10.5271/sjweh.193 · Cited by 38
Reference Guide on StatisticsPreprint
Kaye, D. H., & Freedman, D. A. (2011) · SSRN Electronic Journal · Cited by 38
Accounting for uncertainty about investigator bias: disclosure is informative
Greenland, S. (2009) · Journal of Epidemiology & Community Health · doi:10.1136/jech.2008.084913 · Cited by 37
Self-citation[Testing Precise Hypotheses]: Comment
Casella, G., & Berger, R. L. (1987) · Statistical Science · doi:10.1214/ss/1177013243 · Cited by 37
The “significance” crisis in psychology and education
Thompson, B. (2004) · The Journal of Socio-Economics · doi:10.1016/j.socec.2004.09.034 · Cited by 37
Koren, J. (2016) · University of Michigan Library Repository · Cited by 37
Transparency and disclosure, neutrality and balance: shared values or just shared words?
Greenland, S. (2012) · Journal of Epidemiology & Community Health · doi:10.1136/jech-2011-200459 · Cited by 35
Self-citationGreenland, S., & Poole, C. (2012) · Epidemiology · doi:10.1097/ede.0b013e3182785a49 · Cited by 33
Self-citationTests of significance considered as evidence*
Berkson, J. (2003) · International Journal of Epidemiology · doi:10.1093/ije/dyg255 · Cited by 28
How to test hypotheses if you must
Grieve, A. P. (2015) · Pharmaceutical Statistics · doi:10.1002/pst.1667 · Cited by 19
The summarizing of clinical experiments by significance levels
Anscombe, F. J. (1990) · Statistics in Medicine · doi:10.1002/sim.4780090617 · Cited by 18
A Prevalent Misconception About Sample Size, Statistical Significance, and Clinical Importance
Bandt, C. L., & Boen, J. R. (1972) · Journal of Periodontology · doi:10.1902/jop.1972.43.3.181 · Cited by 14
Statistical limitations in relation to sample size.
Land, C. E. (1981) · Environmental Health Perspectives · doi:10.1289/ehp.814215 · Cited by 12
Confidence limits vs power, calculations.
Goodman, S. N. (1994) · PubMed · Cited by 5
Self-citationGoodman, S. N. (1994) · Epidemiology · doi:10.1097/00001648-199403000-00025 · Cited by 5
Self-citation&NA; (1998) · Epidemiology · doi:10.1097/00001648-199801000-00004 · Cited by 1
Flagged first, then by citations
4 of the 135 references couldn’t be matched to a known paper.
Research works printed but not linked: 12
- Ashworth A. Veto on the use of null hypothesis testing and p intervals: right or wrong? Taylor & Francis Editor. 2015. Resources online, http://editorresources.taylorandfrancisgroup.com/veto-on-the-use-of-null-hypothesis-testing-and-p-intervals-right-or-wrong/ . Accessed 27 Feb 2016.
- Flanagan O. Journal’s ban on null hypothesis significance testing: reactions from the statistical arena. 2015. Stats Life online, https://www.statslife.org.uk/opinion/2114-journal-s-ban-on-null-hypothesis-significance-testing-reactions-from-the-statistical-arena . Accessed 27 Feb 2016.
- Cox DR. The role of significance tests (with discussion). Scand J Stat. 1977;4:49–70.
- Freedman DA, Pisani R, Purves R. Statistics. 4th ed. New York: Norton; 2007.
- Hogben L. Statistical theory. London: Allen and Unwin; 1957.
- Ware JH, Mosteller F, Ingelfinger JA. p-Values. In: Bailar JC, Hoaglin DC, editors. Ch. 8. Medical uses of statistics. 3rd ed. Hoboken, NJ: Wiley; 2009. p. 175–94.
- Gigerenzer G. Mindless statistics. J Socioecon. 2004;33:567–606.
- Greenland S, Poole C. Problems in common interpretations of statistics in scientific articles, expert reports, and testimony. Jurimetrics. 2011;51:113–29.
- Kaye DH. Is proof of statistical significance relevant? Wash Law Rev. 1986;61:1333–66.
- Royall R. Statistical evidence. New York: Chapman and Hall; 1997.
- Greenland S, O’Rourke K. Meta-analysis Ch. 33. In: Rothman KJ, Greenland S, Lash TL, editors. Modern epidemiology. 3rd ed. Philadelphia: Lippincott-Wolters-Kluwer; 2008. p. 682–5.
- Sterne JAC. Meta-analysis: an updated collection from the Stata journal. College Station, TX: Stata Press; 2009.
Cited by
Sorted by citations
Sensitivity analysis in observational research: Introducing the E-value
VanderWeele, T. J., & Ding, P. (2017) · Annals of Internal Medicine · doi:10.7326/m16-2607 · Cited by 6,350
A manifesto for reproducible science
Munafò, M. R., Nosek, B. A., Bishop, D. V. M., Button, K. S., Chambers, C. D., Percie du Sert, N., Simonsohn, U., Wagenmakers, E.-J., Ware, J. J., & Ioannidis, J. P. A. (2017) · Nature Human Behaviour · doi:10.1038/s41562-016-0021 · Cited by 3,397
Bayesian inference for psychology. Part I: Theoretical advantages and practical ramifications
Wagenmakers, E., Marsman, M., Jamil, T., Ly, A., Verhagen, J., Love, J., Selker, R., Gronau, Q. F., Šmíra, M., Epskamp, S., et al. (2017) · Psychonomic Bulletin & Review · doi:10.3758/s13423-017-1343-3 · Cited by 1,767
Kruschke, J. K., & Liddell, T. M. (2018) · Psychonomic Bulletin & Review · doi:10.3758/s13423-016-1221-4 · Cited by 1,288
Lederer, D. J., Bell, S. C., Branson, R. D., Chalmers, J. D., Marshall, R., Maslove, D. M., Ost, D. E., Punjabi, N. M., Schatz, M., Smyth, A. R., et al. (2019) · Annals of the American Thoracic Society · doi:10.1513/annalsats.201808-564ps · Cited by 715
Metadata
- Authors
- Greenland, S., Senn, S. J., Rothman, K. J., Carlin, J. B., Poole, C., Goodman, S. N., & Altman, D. G.
- Affiliations
- University of California, Los Angeles (US)
- Luxembourg Institute of Health (LU)
- RTI International (US)
- The University of Melbourne (AU)
- University of North Carolina at Chapel Hill (US)
- Stanford Medicine (US)
- University of Oxford (GB)
- Corresponding author
- Greenland, Sander
- ORCID
- Venue
- European Journal of Epidemiology
- Published
- 2016-04
- Volume
- 31
- Issue
- 4
- Pages
- 337-350
- ISSN
- 0393-2990 (ISSN-L)
- 1573-7284
- PMC
- PMC4877414
- OpenAlex
- W2408017125
- Cited by
- 3,200
- References
- 135
- Citations by year
- 2026: 325 · 2025: 454 · 2024: 407 · 2023: 290 · 2022: 348 · 2021: 392 · 2020: 330 · 2019: 270 · 2018: 201 · 2017: 148 · 2016: 33
- FWCI
- 220.87
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- European Journal of Epidemiology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by)
- Funders
- Seventh Framework Programme (602552)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistics Education and Methodologies
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- p-values, confidence intervals, statistical power, statistical misinterpretation, hypothesis testing, selective analysis, analysis protocol, statistical reporting guidelines, statistics education
- MeSH terms
- Data Interpretation, Statistical, Humans, Probability, Confidence Intervals
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review