Contents 1 History 1.1 Related concepts 2 Role in statistical hypothesis testing 2.1 Stringent significance thresholds in specific fields 3 Limitations 3.1 Effect size 3.2 Reproducibility 4 Challenges 4.1 Overuse in some journals 4.2 Redefining significance 5 See also 6 References 7 Further reading 8 External links

History[edit] Main article: History of statistics In 1925, Ronald Fisher advanced the idea of statistical hypothesis testing, which he called "tests of significance", in his publication Statistical Methods for Research Workers.[18][19][20] Fisher suggested a probability of one in twenty (0.05) as a convenient cutoff level to reject the null hypothesis.[21] In a 1933 paper, Jerzy Neyman and Egon Pearson called this cutoff the significance level, which they named α. They recommended that α be set ahead of time, prior to any data collection.[21][22] Despite his initial suggestion of 0.05 as a significance level, Fisher did not intend this cutoff value to be fixed. In his 1956 publication Statistical methods and scientific inference, he recommended that significance levels be set according to specific circumstances.[21] Related concepts[edit] The significance level α is the threshold for p below which the experimenter assumes the null hypothesis is false, and something else is going on. This means α is also the probability of mistakenly rejecting the null hypothesis, if the null hypothesis is true.[23] Sometimes researchers talk about the confidence level γ = (1 − α) instead. This is the probability of not rejecting the null hypothesis given that it is true.[24][25] Confidence levels and confidence intervals were introduced by Neyman in 1937.[26]

Role in statistical hypothesis testing[edit] Main articles: Statistical hypothesis testing, Null hypothesis, Alternative hypothesis, p-value, and Type I and type II errors In a two-tailed test, the rejection region for a significance level of α=0.05 is partitioned to both ends of the sampling distribution and makes up 5% of the area under the curve (white areas). Statistical significance plays a pivotal role in statistical hypothesis testing. It is used to determine whether the null hypothesis should be rejected or retained. The null hypothesis is the default assumption that nothing happened or changed.[27] For the null hypothesis to be rejected, an observed result has to be statistically significant, i.e. the observed p-value is less than the pre-specified significance level. To determine whether a result is statistically significant, a researcher calculates a p-value, which is the probability of observing an effect given that the null hypothesis is true.[11] The null hypothesis is rejected if the p-value is less than a predetermined level, α. α is called the significance level, and is the probability of rejecting the null hypothesis given that it is true (a type I error). It is usually set at or below 5%. For example, when α is set to 5%, the conditional probability of a type I error, given that the null hypothesis is true, is 5%,[28] and a statistically significant result is one where the observed p-value is less than 5%.[29] When drawing data from a sample, this means that the rejection region comprises 5% of the sampling distribution.[30] These 5% can be allocated to one side of the sampling distribution, as in a one-tailed test, or partitioned to both sides of the distribution as in a two-tailed test, with each tail (or rejection region) containing 2.5% of the distribution. The use of a one-tailed test is dependent on whether the research question or alternative hypothesis specifies a direction such as whether a group of objects is heavier or the performance of students on an assessment is better.[3] A two-tailed test may still be used but it will be less powerful than a one-tailed test because the rejection region for a one-tailed test is concentrated on one end of the null distribution and is twice the size (5% vs. 2.5%) of each rejection region for a two-tailed test. As a result, the null hypothesis can be rejected with a less extreme result if a one-tailed test was used.[31] The one-tailed test is only more powerful than a two-tailed test if the specified direction of the alternative hypothesis is correct. If it is wrong, however, then the one-tailed test has no power. Stringent significance thresholds in specific fields[edit] Main articles: Standard deviation and Normal distribution In specific fields such as particle physics and manufacturing, statistical significance is often expressed in multiples of the standard deviation or sigma (σ) of a normal distribution, with significance thresholds set at a much stricter level (e.g. 5σ).[32][33] For instance, the certainty of the Higgs boson particle's existence was based on the 5σ criterion, which corresponds to a p-value of about 1 in 3.5 million.[33][34] In other fields of scientific research such as genome-wide association studies significance levels as low as 6992500000000000000♠5×10−8 are not uncommon,[35][36] because the number of tests performed is extremely large.

Limitations[edit] Researchers focusing solely on whether their results are statistically significant might report findings that are not substantive[37] and not replicable.[38][39] There is also a difference between statistical significance and practical significance. A study that is found to be statistically significant, may not necessarily be practically significant.[40] Effect size[edit] Main article: Effect size Effect size is a measure of a study's practical significance.[40] A statistically significant result may have a weak effect. To gauge the research significance of their result, researchers are encouraged to always report an effect size along with p-values. An effect size measure quantifies the strength of an effect, such as the distance between two means in units of standard deviation (cf. Cohen's d), the correlation between two variables or its square, and other measures.[41] Reproducibility[edit] Main article: Reproducibility A statistically significant result may not be easy to reproduce.[39] In particular, some statistically significant results will in fact be false positives. Each failed attempt to reproduce a result increases the likelihood that the result was a false positive.[42]

Challenges[edit] Overuse in some journals[edit] Starting in the 2010s, some journals began questioning whether significance testing, and particularly using a threshold of α=5%, was being relied on too heavily as the primary measure of validity of a hypothesis.[43] Some journals encouraged authors to do more detailed analysis than just a statistical significance test. In social psychology, the Journal of Basic and Applied Social Psychology banned the use of significance testing altogether from papers it published,[44] requiring authors to use other measures to evaluate hypotheses and impact.[45][46] Other editors, commenting on this ban have noted: "Banning the reporting of p-values, as Basic and Applied Social Psychology recently did, is not going to solve the problem because it is merely treating a symptom of the problem. There is nothing wrong with hypothesis testing and p-values per se as long as authors, reviewers, and action editors use them correctly." [47] Using Bayesian statistics can improve confidence levels but also requires making additional assumptions,[48] and may not necessarily improve practice regarding statistical testing.[49] Redefining significance[edit] In 2016, the American Statistical Association (ASA) published a statement on p-values, saying that "the widespread use of 'statistical significance' (generally interpreted as 'p≤0.05') as a license for making a claim of a scientific finding (or implied truth) leads to considerable distortion of the scientific process".[48] In 2017, a group of 72 authors proposed to enhance reproducibility by changing the p-value threshold for statistical significance from 0.05 to 0.005.[50] Other researchers responded that imposing a more stringent significance threshold would aggravate problems such as data dredging; alternative propositions are thus to select and justify flexible p-value thresholds before collecting data,[51] or to interpret p-values as continuous indices, thereby discarding thresholds and statistical significance.[52]

See also[edit] Statistics portal A/B testing, ABX test Fisher's method for combining independent tests of significance Look-elsewhere effect Multiple comparisons problem Sample size Texas sharpshooter fallacy (gives examples of tests where the significance level was set too high)

References[edit] ^ a b c Sirkin, R. Mark (2005). "Two-sample t tests". Statistics for the Social Sciences (3rd ed.). Thousand Oaks, CA: SAGE Publications, Inc. pp. 271–316. ISBN 1-412-90546-X.  ^ a b Borror, Connie M. (2009). "Statistical decision making". The Certified Quality Engineer Handbook (3rd ed.). Milwaukee, WI: ASQ Quality Press. pp. 418–472. ISBN 0-873-89745-5.  ^ a b Myers, Jerome L.; Well, Arnold D.; Lorch Jr., Robert F. (2010). "Developing fundamentals of hypothesis testing using the binomial distribution". Research design and statistical analysis (3rd ed.). New York, NY: Routledge. pp. 65–90. ISBN 0-805-86431-8.  ^ Schlotzhauer, Sandra (2007). Elementary Statistics Using JMP (SAS Press) (PAP/CDR ed.). Cary, NC: SAS Institute. pp. 166–169. ISBN 1-599-94375-1.  ^ Johnson, Valen E. (October 9, 2013). "Revised standards for statistical evidence". Proceedings of the National Academy of Sciences. National Academies of Science. 110: 19313–19317. doi:10.1073/pnas.1313476110. Retrieved 3 July 2014.  ^ Redmond, Carol; Colton, Theodore (2001). "Clinical significance versus statistical significance". Biostatistics in Clinical Trials. Wiley Reference Series in Biostatistics (3rd ed.). West Sussex, United Kingdom: John Wiley & Sons Ltd. pp. 35–36. ISBN 0-471-82211-6.  ^ Cumming, Geoff (2012). Understanding The New Statistics: Effect Sizes, Confidence Intervals, and Meta-Analysis. New York, USA: Routledge. pp. 27–28.  ^ Krzywinski, Martin; Altman, Naomi (30 October 2013). "Points of significance: Significance, P values and t-tests". Nature Methods. Nature Publishing Group. 10 (11): 1041–1042. doi:10.1038/nmeth.2698. Retrieved 3 July 2014.  ^ Sham, Pak C.; Purcell, Shaun M (17 April 2014). "Statistical power and significance testing in large-scale genetic studies". Nature Reviews Genetics. Nature Publishing Group. 15 (5): 335–346. doi:10.1038/nrg3706. Retrieved 3 July 2014.  ^ Altman, Douglas G. (1999). Practical Statistics for Medical Research. New York, USA: Chapman & Hall/CRC. p. 167. ISBN 978-0412276309.  ^ a b Devore, Jay L. (2011). Probability and Statistics for Engineering and the Sciences (8th ed.). Boston, MA: Cengage Learning. pp. 300–344. ISBN 0-538-73352-7.  ^ Craparo, Robert M. (2007). "Significance level". In Salkind, Neil J. Encyclopedia of Measurement and Statistics. 3. Thousand Oaks, CA: SAGE Publications. pp. 889–891. ISBN 1-412-91611-9.  ^ Sproull, Natalie L. (2002). "Hypothesis testing". Handbook of Research Methods: A Guide for Practitioners and Students in the Social Science (2nd ed.). Lanham, MD: Scarecrow Press, Inc. pp. 49–64. ISBN 0-810-84486-9.  ^ Babbie, Earl R. (2013). "The logic of sampling". The Practice of Social Research (13th ed.). Belmont, CA: Cengage Learning. pp. 185–226. ISBN 1-133-04979-6.  ^ Faherty, Vincent (2008). "Probability and statistical significance". Compassionate Statistics: Applied Quantitative Analysis for Social Services (With exercises and instructions in SPSS) (1st ed.). Thousand Oaks, CA: SAGE Publications, Inc. pp. 127–138. ISBN 1-412-93982-8.  ^ McKillup, Steve (2006). "Probability helps you make a decision about your results". Statistics Explained: An Introductory Guide for Life Scientists (1st ed.). Cambridge, United Kingdom: Cambridge University Press. pp. 44–56. ISBN 0-521-54316-9.  ^ Myers, Jerome L.; Well, Arnold D.; Lorch Jr, Robert F. (2010). "The t distribution and its applications". Research Design and Statistical Analysis: Third Edition (3rd ed.). New York, NY: Routledge. pp. 124–153. ISBN 0-805-86431-8.  ^ Cumming, Geoff (2011). "From null hypothesis significance to testing effect sizes". Understanding The New Statistics: Effect Sizes, Confidence Intervals, and Meta-Analysis. Multivariate Applications Series. East Sussex, United Kingdom: Routledge. pp. 21–52. ISBN 0-415-87968-X.  ^ Fisher, Ronald A. (1925). Statistical Methods for Research Workers. Edinburgh, UK: Oliver and Boyd. p. 43. ISBN 0-050-02170-2.  ^ Poletiek, Fenna H. (2001). "Formal theories of testing". Hypothesis-testing Behaviour. Essays in Cognitive Psychology (1st ed.). East Sussex, United Kingdom: Psychology Press. pp. 29–48. ISBN 1-841-69159-3.  ^ a b c Quinn, Geoffrey R.; Keough, Michael J. (2002). Experimental Design and Data Analysis for Biologists (1st ed.). Cambridge, UK: Cambridge University Press. pp. 46–69. ISBN 0-521-00976-6.  ^ Neyman, J.; Pearson, E.S. (1933). "The testing of statistical hypotheses in relation to probabilities a priori". Mathematical Proceedings of the Cambridge Philosophical Society. 29: 492–510. doi:10.1017/S030500410001152X.  ^ Schlotzhauer, Sandra (2007). Elementary Statistics Using JMP (SAS Press) (PAP/CDR ed.). Cary, NC: SAS Institute. pp. 166–169. ISBN 1-599-94375-1.  ^ "Conclusions about statistical significance are possible with the help of the confidence interval. If the confidence interval does not include the value of zero effect, it can be assumed that there is a statistically significant result." "Confidence Interval or P-Value?". doi:10.3238/arztebl.2009.0335.  ^ StatNews #73: Overlapping Confidence Intervals and Statistical Significance ^ Neyman, J. (1937). "Outline of a Theory of Statistical Estimation Based on the Classical Theory of Probability". Philosophical Transactions of the Royal Society A. 236: 333–380. doi:10.1098/rsta.1937.0005.  ^ Meier, Kenneth J.; Brudney, Jeffrey L.; Bohte, John (2011). Applied Statistics for Public and Nonprofit Administration (3rd ed.). Boston, MA: Cengage Learning. pp. 189–209. ISBN 1-111-34280-6.  ^ Healy, Joseph F. (2009). The Essentials of Statistics: A Tool for Social Research (2nd ed.). Belmont, CA: Cengage Learning. pp. 177–205. ISBN 0-495-60143-8.  ^ McKillup, Steve (2006). Statistics Explained: An Introductory Guide for Life Scientists (1st ed.). Cambridge, UK: Cambridge University Press. pp. 32–38. ISBN 0-521-54316-9.  ^ Health, David (1995). An Introduction To Experimental Design And Statistics For Biology (1st ed.). Boston, MA: CRC press. pp. 123–154. ISBN 1-857-28132-2.  ^ Hinton, Perry R. (2010). "Significance, error, and power". Statistics explained (3rd ed.). New York, NY: Routledge. pp. 79–90. ISBN 1-848-72312-1.  ^ Vaughan, Simon (2013). Scientific Inference: Learning from Data (1st ed.). Cambridge, UK: Cambridge University Press. pp. 146–152. ISBN 1-107-02482-X.  ^ a b Bracken, Michael B. (2013). Risk, Chance, and Causation: Investigating the Origins and Treatment of Disease (1st ed.). New Haven, CT: Yale University Press. pp. 260–276. ISBN 0-300-18884-6.  ^ Franklin, Allan (2013). "Prologue: The rise of the sigmas". Shifting Standards: Experiments in Particle Physics in the Twentieth Century (1st ed.). Pittsburgh, PA: University of Pittsburgh Press. pp. Ii–Iii. ISBN 0-822-94430-8.  ^ Clarke, GM; Anderson, CA; Pettersson, FH; Cardon, LR; Morris, AP; Zondervan, KT (February 6, 2011). "Basic statistical analysis in genetic case-control studies". Nature Protocols. 6 (2): 121–33. doi:10.1038/nprot.2010.182. PMC 3154648 . PMID 21293453.  ^ Barsh, GS; Copenhaver, GP; Gibson, G; Williams, SM (July 5, 2012). "Guidelines for Genome-Wide Association Studies". PLoS Genetics. 8 (7): e1002812. doi:10.1371/journal.pgen.1002812. PMC 3390399 . PMID 22792080.  ^ Carver, Ronald P. (1978). "The Case Against Statistical Significance Testing". Harvard Educational Review. 48: 378–399.  ^ Ioannidis, John P. A. (2005). "Why most published research findings are false". PLoS Medicine. 2: e124. doi:10.1371/journal.pmed.0020124. PMC 1182327 . PMID 16060722.  ^ a b Amrhein, Valentin; Korner-Nievergelt, Fränzi; Roth, Tobias (2017). "The earth is flat (p > 0.05): significance thresholds and the crisis of unreplicable research". PeerJ. 5: e3544. doi:10.7717/peerj.3544.  ^ a b Hojat, Mohammadreza; Xu, Gang (2004). "A Visitor's Guide to Effect Sizes". Advances in Health Sciences Education.  |access-date= requires |url= (help) ^ Pedhazur, Elazar J.; Schmelkin, Liora P. (1991). Measurement, Design, and Analysis: An Integrated Approach (Student ed.). New York, NY: Psychology Press. pp. 180–210. ISBN 0-805-81063-3.  ^ Stahel, Werner (2016). "Statistical Issue in Reproducibility". Principles, Problems, Practices, and Prospects Reproducibility: Principles, Problems, Practices, and Prospects: 87–114.  |access-date= requires |url= (help) ^ "CSSME Seminar Series: The argument over p-values and the Null Hypothesis Significance Testing (NHST) paradigm  » School of Education  » University of Leeds". Retrieved 2016-12-01.  ^ Novella, Steven (February 25, 2015). "Psychology Journal Bans Significance Testing". Science-Based Medicine.  ^ Woolston, Chris (2015-03-05). "Psychology journal bans P values". Nature. 519 (7541): 9–9. doi:10.1038/519009f.  ^ Siegfried, Tom (2015-03-17). "P value ban: small step for a journal, giant leap for science". Science News. Retrieved 2016-12-01.  ^ Antonakis, John (February 2017). "On doing better science: From thrill of discovery to policy implications". The Leadership Quarterly. 28 (1): 5–21. doi:10.1016/j.leaqua.2017.01.006.  ^ a b Wasserstein, Ronald L.; Lazar, Nicole A. (2016-04-02). "The ASA's Statement on p-Values: Context, Process, and Purpose". The American Statistician. 70 (2): 129–133. doi:10.1080/00031305.2016.1154108. ISSN 0003-1305.  ^ García-Pérez, Miguel A. (2016-10-05). "Thou Shalt Not Bear False Witness Against Null Hypothesis Significance Testing". Educational and Psychological Measurement: 0013164416668232. doi:10.1177/0013164416668232. ISSN 0013-1644.  ^ Benjamin, Daniel; et al. (2017). "Redefine statistical significance". Nature Human Behaviour. 1: 0189. doi:10.1038/s41562-017-0189-z. CS1 maint: Explicit use of et al. (link) ^ Chawla, Dalmeet (2017). "'One-size-fits-all' threshold for P values under fire". Nature. doi:10.1038/nature.2017.22625.  ^ Amrhein, Valentin; Greenland, Sander (2017). "Remove, rather than redefine, statistical significance". Nature Human Behaviour. 1: 0224. doi:10.1038/s41562-017-0224-0. 

Further reading[edit] Ziliak, Stephen and Deirdre McCloskey (2008), The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives. Ann Arbor, University of Michigan Press, 2009. ISBN 978-0-472-07007-7. Reviews and reception: (compiled by Ziliak) Thompson, Bruce (2004). "The "significance" crisis in psychology and education". Journal of Socio-Economics. 33: 607–613. doi:10.1016/j.socec.2004.09.034.  Chow, Siu L., (1996). Statistical Significance: Rationale, Validity and Utility, Volume 1 of series Introducing Statistical Methods, Sage Publications Ltd, ISBN 978-0-7619-5205-3 – argues that statistical significance is useful in certain circumstances. Kline, Rex, (2004). Beyond Significance Testing: Reforming Data Analysis Methods in Behavioral Research Washington, DC: American Psychological Association. Nuzzo, Regina (2014). Scientific method: Statistical errors. Nature Vol. 506, p. 150-152 (open access). Highlights common misunderstandings about the p value. Cohen, Joseph (1994). [1]. The earth is round (p<.05). American Psychologist. Vol 49, p. 997-1003. Reviews problems with null hypothesis statistical testing.

External links[edit] Wikiversity has learning resources about Statistical significance The article "Earliest Known Uses of Some of the Words of Mathematics (S)" contains an entry on Significance that provides some historical information. "The Concept of Statistical Significance Testing" (February 1994): article by Bruce Thompon hosted by the ERIC Clearinghouse on Assessment and Evaluation, Washington, D.C. "What does it mean for a result to be "statistically significant"?" (no date): an article from the Statistical Assessment Service at George Mason University, Washington, D.C. v t e Statistics Outline Index Descriptive statistics Continuous data Center Mean arithmetic geometric harmonic Median Mode Dispersion Variance Standard deviation Coefficient of variation Percentile Range Interquartile range Shape Central limit theorem Moments Skewness Kurtosis L-moments Count data Index of dispersion Summary tables Grouped data Frequency distribution Contingency table Dependence Pearson product-moment correlation Rank correlation Spearman's rho Kendall's tau Partial correlation Scatter plot Graphics Bar chart Biplot Box plot Control chart Correlogram Fan chart Forest plot Histogram Pie chart Q–Q plot Run chart Scatter plot Stem-and-leaf display Radar chart Data collection Study design Population Statistic Effect size Statistical power Sample size determination Missing data Survey methodology Sampling stratified cluster Standard error Opinion poll Questionnaire Controlled experiments Design control optimal Controlled trial Randomized Random assignment Replication Blocking Interaction Factorial experiment Uncontrolled studies Observational study Natural experiment Quasi-experiment Statistical inference Statistical theory Population Statistic Probability distribution Sampling distribution Order statistic Empirical distribution Density estimation Statistical model Lp space Parameter location scale shape Parametric family Likelihood (monotone) Location–scale family Exponential family Completeness Sufficiency Statistical functional Bootstrap U V Optimal decision loss function Efficiency Statistical distance divergence Asymptotics Robustness Frequentist inference Point estimation Estimating equations Maximum likelihood Method of moments M-estimator Minimum distance Unbiased estimators Mean-unbiased minimum-variance Rao–Blackwellization Lehmann–Scheffé theorem Median unbiased Plug-in Interval estimation Confidence interval Pivot Likelihood interval Prediction interval Tolerance interval Resampling Bootstrap Jackknife Testing hypotheses 1- & 2-tails Power Uniformly most powerful test Permutation test Randomization test Multiple comparisons Parametric tests Likelihood-ratio Wald Score Specific tests Z (normal) Student's t-test F Goodness of fit Chi-squared Kolmogorov–Smirnov Anderson–Darling Lilliefors Jarque–Bera Normality (Shapiro–Wilk) Likelihood-ratio test Model selection Cross validation AIC BIC Rank statistics Sign Sample median Signed rank (Wilcoxon) Hodges–Lehmann estimator Rank sum (Mann–Whitney) Nonparametric anova 1-way (Kruskal–Wallis) 2-way (Friedman) Ordered alternative (Jonckheere–Terpstra) Bayesian inference Bayesian probability prior posterior Credible interval Bayes factor Bayesian estimator Maximum posterior estimator Correlation Regression analysis Correlation Pearson product-moment Partial correlation Confounding variable Coefficient of determination Regression analysis Errors and residuals Regression model validation Mixed effects models Simultaneous equations models Multivariate adaptive regression splines (MARS) Linear regression Simple linear regression Ordinary least squares General linear model Bayesian regression Non-standard predictors Nonlinear regression Nonparametric Semiparametric Isotonic Robust Heteroscedasticity Homoscedasticity Generalized linear model Exponential families Logistic (Bernoulli) / Binomial / Poisson regressions Partition of variance Analysis of variance (ANOVA, anova) Analysis of covariance Multivariate ANOVA Degrees of freedom Categorical / Multivariate / Time-series / Survival analysis Categorical Cohen's kappa Contingency table Graphical model Log-linear model McNemar's test Multivariate Regression Manova Principal components Canonical correlation Discriminant analysis Cluster analysis Classification Structural equation model Factor analysis Multivariate distributions Elliptical distributions Normal Time-series General Decomposition Trend Stationarity Seasonal adjustment Exponential smoothing Cointegration Structural break Granger causality Specific tests Dickey–Fuller Johansen Q-statistic (Ljung–Box) Durbin–Watson Breusch–Godfrey Time domain Autocorrelation (ACF) partial (PACF) Cross-correlation (XCF) ARMA model ARIMA model (Box–Jenkins) Autoregressive conditional heteroskedasticity (ARCH) Vector autoregression (VAR) Frequency domain Spectral density estimation Fourier analysis Wavelet Survival Survival function Kaplan–Meier estimator (product limit) Proportional hazards models Accelerated failure time (AFT) model First hitting time Hazard function Nelson–Aalen estimator Test Log-rank test Applications Biostatistics Bioinformatics Clinical trials / studies Epidemiology Medical statistics Engineering statistics Chemometrics Methods engineering Probabilistic design Process / quality control Reliability System identification Social statistics Actuarial science Census Crime statistics Demography Econometrics National accounts Official statistics Population statistics Psychometrics Spatial statistics Cartography Environmental statistics Geographic information system Geostatistics Kriging Category Portal Commons WikiProject Retrieved from "" Categories: Statistical hypothesis testingHidden categories: Pages using citations with accessdate and no URLCS1 maint: Explicit use of et al.

Navigation menu Personal tools Not logged inTalkContributionsCreate accountLog in Namespaces ArticleTalk Variants Views ReadEditView history More Search Navigation Main pageContentsFeatured contentCurrent eventsRandom articleDonate to WikipediaWikipedia store Interaction HelpAbout WikipediaCommunity portalRecent changesContact page Tools What links hereRelated changesUpload fileSpecial pagesPermanent linkPage informationWikidata itemCite this page Print/export Create a bookDownload as PDFPrintable version Languages العربيةAzərbaycancaCatalàČeštinaDanskDeutschΕλληνικάEspañolEuskaraفارسیFrançais한국어ÍslenskaItalianoעבריתLietuviųМакедонскиNederlands日本語NorskPolskiPortuguêsРусскийSimple EnglishSuomiSvenskaไทยTürkçeУкраїнськаTiếng Việt粵語中文 Edit links This page was last edited on 31 December 2017, at 23:02. Text is available under the Creative Commons Attribution-ShareAlike License; additional terms may apply. By using this site, you agree to the Terms of Use and Privacy Policy. Wikipedia® is a registered trademark of the Wikimedia Foundation, Inc., a non-profit organization. Privacy policy About Wikipedia Disclaimers Contact Wikipedia Developers Cookie statement Mobile view (window.RLQ=window.RLQ||[]).push(function(){mw.config.set({"wgPageParseReport":{"limitreport":{"cputime":"0.652","walltime":"0.742","ppvisitednodes":{"value":4111,"limit":1000000},"ppgeneratednodes":{"value":0,"limit":1500000},"postexpandincludesize":{"value":297390,"limit":2097152},"templateargumentsize":{"value":25526,"limit":2097152},"expansiondepth":{"value":15,"limit":40},"expensivefunctioncount":{"value":0,"limit":500},"entityaccesscount":{"value":0,"limit":400},"timingprofile":["100.00% 620.386 1 -total"," 58.58% 363.404 1 Template:Reflist"," 27.04% 167.728 27 Template:Cite_book"," 17.76% 110.164 1 Template:Statistics"," 17.03% 105.652 1 Template:Navbox_with_collapsible_groups"," 16.34% 101.357 21 Template:Cite_journal"," 9.72% 60.280 11 Template:Navbox"," 6.14% 38.089 1 Template:Val"," 5.77% 35.774 2 Template:Isbn"," 4.19% 26.022 5 Template:Main"]},"scribunto":{"limitreport-timeusage":{"value":"0.352","limit":"10.000"},"limitreport-memusage":{"value":7148593,"limit":52428800}},"cachereport":{"origin":"mw1269","timestamp":"20180115101639","ttl":1900800,"transientcontent":false}}});});(window.RLQ=window.RLQ||[]).push(function(){mw.config.set({"wgBackendResponseTime":856,"wgHostname":"mw1269"});});

Statistical_significance - Photos and All Basic Informations

Statistical_significance More Links

Statistical Hypothesis TestingNull HypothesisSignificance LevelP-valueExperimentObservational StudySample (statistics)Statistical PopulationSampling ErrorClinical SignificanceHistory Of StatisticsRonald FisherJerzy NeymanEgon PearsonConfidence LevelStatistical Hypothesis TestingNull HypothesisAlternative HypothesisP-valueType I And Type II ErrorsEnlargeTwo-tailed TestSampling DistributionNull HypothesisType I ErrorConditional ProbabilityType I ErrorSampling DistributionOne-tailed TestTwo-tailed TestResearch QuestionAlternative HypothesisStatistical PowerStandard DeviationNormal DistributionParticle PhysicsManufacturingStandard DeviationNormal DistributionHiggs BosonGenome-wide Association StudyEffect SizeEffect SizeCohen's DPearson Product-moment Correlation CoefficientCoefficient Of DeterminationReproducibilityBayesian StatisticsAmerican Statistical AssociationData DredgingPortal:StatisticsA/B TestingABX TestFisher's MethodStatistical IndependenceStatistical Hypothesis TestingLook-elsewhere EffectMultiple Comparisons ProblemSample SizeTexas Sharpshooter FallacyInternational Standard Book NumberSpecial:BookSources/1-412-90546-XInternational Standard Book NumberSpecial:BookSources/0-873-89745-5International Standard Book NumberSpecial:BookSources/0-805-86431-8International Standard Book NumberSpecial:BookSources/1-599-94375-1Digital Object IdentifierInternational Standard Book NumberSpecial:BookSources/0-471-82211-6Digital Object IdentifierDigital Object IdentifierInternational Standard Book NumberSpecial:BookSources/978-0412276309International Standard Book NumberSpecial:BookSources/0-538-73352-7International Standard Book NumberSpecial:BookSources/1-412-91611-9International Standard Book NumberSpecial:BookSources/0-810-84486-9International Standard Book NumberSpecial:BookSources/1-133-04979-6International Standard Book NumberSpecial:BookSources/1-412-93982-8International Standard Book NumberSpecial:BookSources/0-521-54316-9International Standard Book NumberSpecial:BookSources/0-805-86431-8International Standard Book NumberSpecial:BookSources/0-415-87968-XInternational Standard Book NumberSpecial:BookSources/0-050-02170-2International Standard Book NumberSpecial:BookSources/1-841-69159-3International Standard Book NumberSpecial:BookSources/0-521-00976-6Digital Object IdentifierInternational Standard Book NumberSpecial:BookSources/1-599-94375-1Digital Object IdentifierJerzy NeymanPhilosophical Transactions Of The Royal Society ADigital Object IdentifierInternational Standard Book NumberSpecial:BookSources/1-111-34280-6International Standard Book NumberSpecial:BookSources/0-495-60143-8International Standard Book NumberSpecial:BookSources/0-521-54316-9International Standard Book NumberSpecial:BookSources/1-857-28132-2International Standard Book NumberSpecial:BookSources/1-848-72312-1International Standard Book NumberSpecial:BookSources/1-107-02482-XInternational Standard Book NumberSpecial:BookSources/0-300-18884-6International Standard Book NumberSpecial:BookSources/0-822-94430-8Digital Object IdentifierPubMed CentralPubMed IdentifierDigital Object IdentifierPubMed CentralPubMed IdentifierDigital Object IdentifierPubMed CentralPubMed IdentifierDigital Object IdentifierHelp:CS1 ErrorsInternational Standard Book NumberSpecial:BookSources/0-805-81063-3Help:CS1 ErrorsDigital Object IdentifierDigital Object IdentifierDigital Object IdentifierInternational Standard Serial NumberDigital Object IdentifierInternational Standard Serial NumberDigital Object IdentifierCategory:CS1 Maint: Explicit Use Of Et Al.Digital Object IdentifierDigital Object IdentifierStephen ZiliakDeirdre McCloskeyUniversity Of Michigan PressInternational Standard Book NumberSpecial:BookSources/978-0-472-07007-7Digital Object IdentifierInternational Standard Book NumberSpecial:BookSources/978-0-7619-5205-3Template:StatisticsTemplate Talk:StatisticsStatisticsOutline Of StatisticsList Of Statistics ArticlesDescriptive StatisticsContinuous Probability DistributionCentral TendencyMeanArithmetic MeanGeometric MeanHarmonic MeanMedianMode (statistics)Statistical DispersionVarianceStandard DeviationCoefficient Of VariationPercentileRange (statistics)Interquartile RangeShape Of The DistributionCentral Limit TheoremMoment (mathematics)SkewnessKurtosisL-momentCount DataIndex Of DispersionGrouped DataFrequency DistributionContingency TableCorrelation And DependencePearson Correlation CoefficientRank CorrelationSpearman's Rank Correlation CoefficientKendall Tau Rank Correlation CoefficientPartial CorrelationScatter PlotStatistical GraphicsBar ChartBiplotBox PlotControl ChartCorrelogramFan Chart (statistics)Forest PlotHistogramPie ChartQ–Q PlotRun ChartScatter PlotStem-and-leaf DisplayRadar ChartData CollectionPopulation (statistics)StatisticEffect SizeStatistical PowerSample Size DeterminationMissing DataSurvey MethodologySampling (statistics)Stratified SamplingCluster SamplingStandard ErrorOpinion PollQuestionnaireExperimentDesign Of ExperimentsScientific ControlOptimal DesignRandomized Controlled TrialRandomized ExperimentRandom AssignmentReplication (statistics)Blocking (statistics)Interaction (statistics)Factorial ExperimentObservational StudyNatural ExperimentQuasi-experimentStatistical InferenceStatistical TheoryPopulation (statistics)StatisticProbability DistributionSampling DistributionOrder StatisticEmpirical Distribution FunctionDensity EstimationStatistical ModelLp SpaceStatistical ParameterLocation ParameterScale ParameterShape ParameterParametric StatisticsLikelihood FunctionMonotone Likelihood RatioLocation–scale FamilyExponential FamilyCompleteness (statistics)Sufficient StatisticPlug-in PrincipleBootstrapping (statistics)U-statisticV-statisticOptimal DecisionLoss FunctionEfficiency (statistics)Statistical DistanceDivergence (statistics)Asymptotic Theory (statistics)Robust StatisticsFrequentist InferencePoint EstimationEstimating EquationsMaximum LikelihoodMethod Of Moments (statistics)M-estimatorMinimum Distance EstimationBias Of An EstimatorMinimum-variance Unbiased EstimatorRao–Blackwell TheoremLehmann–Scheffé TheoremMedian-unbiased EstimatorPlug-in PrincipleInterval EstimationConfidence IntervalPivotal QuantityLikelihood IntervalPrediction IntervalTolerance IntervalResampling (statistics)Bootstrapping (statistics)Jackknife ResamplingStatistical Hypothesis TestingOne- And Two-tailed TestsPower (statistics)Uniformly Most Powerful TestPermutation TestRandomization TestMultiple ComparisonsParametric StatisticsLikelihood-ratio TestWald TestScore TestZ-testStudent's T-testF-testGoodness Of FitChi-squared TestKolmogorov–Smirnov TestAnderson–Darling TestLilliefors TestJarque–Bera TestShapiro–Wilk TestLikelihood-ratio TestModel SelectionCross-validation (statistics)Akaike Information CriterionBayesian Information CriterionRank StatisticsSign TestSample MedianWilcoxon Signed-rank TestHodges–Lehmann EstimatorMann–Whitney U TestNonparametric StatisticsAnalysis Of VarianceKruskal–Wallis One-way Analysis Of VarianceFriedman TestJonckheere's Trend TestBayesian InferenceBayesian ProbabilityPrior ProbabilityPosterior ProbabilityCredible IntervalBayes FactorBayes EstimatorMaximum A Posteriori EstimationCorrelation And DependenceRegression AnalysisCorrelation And DependencePearson Product-moment Correlation CoefficientPartial CorrelationConfoundingCoefficient Of DeterminationRegression AnalysisErrors And Residuals In StatisticsRegression Model ValidationMixed ModelSimultaneous Equations ModelMultivariate Adaptive Regression SplinesLinear RegressionSimple Linear RegressionOrdinary Least SquaresGeneral Linear ModelBayesian Linear RegressionNonlinear RegressionNonparametric RegressionSemiparametric RegressionIsotonic RegressionRobust RegressionHeteroscedasticityHomoscedasticityGeneralized Linear ModelExponential FamilyLogistic RegressionBinomial RegressionPoisson RegressionPartition Of Sums Of SquaresAnalysis Of VarianceAnalysis Of CovarianceMultivariate Analysis Of VarianceDegrees Of Freedom (statistics)Categorical VariableMultivariate StatisticsTime SeriesSurvival AnalysisCategorical VariableCohen's KappaContingency TableGraphical ModelPoisson RegressionMcNemar's TestMultivariate StatisticsGeneral Linear ModelMultivariate Analysis Of VariancePrincipal Component AnalysisCanonical CorrelationLinear Discriminant AnalysisCluster AnalysisStatistical ClassificationStructural Equation ModelingFactor AnalysisMultivariate DistributionElliptical DistributionMultivariate Normal DistributionTime SeriesDecomposition Of Time SeriesTrend EstimationStationary ProcessSeasonal AdjustmentExponential SmoothingCointegrationStructural BreakGranger CausalityDickey–Fuller TestJohansen TestLjung–Box TestDurbin–Watson StatisticBreusch–Godfrey TestTime DomainAutocorrelationPartial Autocorrelation FunctionCross-correlationAutoregressive–moving-average ModelBox–Jenkins MethodAutoregressive Conditional HeteroskedasticityVector AutoregressionFrequency DomainSpectral Density EstimationFourier AnalysisWaveletSurvival AnalysisSurvival FunctionKaplan–Meier EstimatorProportional Hazards ModelAccelerated Failure Time ModelFirst-hitting-time ModelFailure RateNelson–Aalen EstimatorLog-rank TestList Of Fields Of Application Of StatisticsBiostatisticsBioinformaticsClinical TrialClinical Study DesignEpidemiologyMedical StatisticsEngineering StatisticsChemometricsMethods EngineeringProbabilistic DesignStatistical Process ControlQuality ControlReliability EngineeringSystem IdentificationSocial StatisticsActuarial ScienceCensusCrime StatisticsDemographic StatisticsEconometricsNational AccountsOfficial StatisticsPopulation StatisticsPsychometricsSpatial AnalysisCartographyEnvironmental StatisticsGeographic Information SystemGeostatisticsKrigingCategory:StatisticsPortal:StatisticsWikipedia:WikiProject StatisticsHelp:CategoryCategory:Statistical Hypothesis TestingCategory:Pages Using Citations With Accessdate And No URLCategory:CS1 Maint: Explicit Use Of Et Al.Discussion About Edits From This IP Address [n]A List Of Edits Made From This IP Address [y]View The Content Page [c]Discussion About The Content Page [t]Edit This Page [e]Visit The Main Page [z]Guides To Browsing WikipediaFeatured Content – The Best Of WikipediaFind Background Information On Current EventsLoad A Random Article [x]Guidance On How To Use And Edit WikipediaFind Out About WikipediaAbout The Project, What You Can Do, Where To Find ThingsA List Of Recent Changes In The Wiki [r]List Of All English Wikipedia Pages Containing Links To This Page [j]Recent Changes In Pages Linked From This Page [k]Upload Files [u]A List Of All Special Pages [q]Wikipedia:AboutWikipedia:General Disclaimer

view link view link view link view link view link