The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Testing Precise Hypotheses
Open accessAbstract
Testing of precise (point or small interval) hypotheses is reviewed, with special emphasis placed on exploring the dramatic conflict between conditional measures (Bayes factors and posterior probabilities) and the classical P-value (or observed significance level). This conflict is highlighted by finding lower bounds on the conditional measures over wide classes of priors, in normal and binomial situations, lower bounds, which are much larger than the P-value; this leads to the recommendation of several alternatives to P-values. Results are also given concerning the validity of approximating an interval null by a point null. The overall discussion features critical examination of issues such as the probability of objective testing and the possibility of testing from confidence sets.
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?
The cited source says what the claim attributes to it.
In the citing paper
“The posterior probability of 'no effect' can be viewed as an approximation to the posterior probability of the effect being 'small,' namely, P lparenOSCASB vertSHT β vertSHT < ε rparenOSCASB , provided that ε is at most about onehalf of a standard error (Berger and Delampady, 1987).”
From Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors (1999) · cites this paper as Berger and Delampady, 1987
The cited source says what the claim attributes to it.
In the citing paper
“In fact, p -values arguably overstate the evidence for an effect even when there is no model uncertainty (Edwards, Lindman and Savage, 1963; Berger and Delampady, 1987; Berger and Sellke, 1987).”
From Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors (1999) · cites this paper as Berger and Delampady, 1987
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
Statistical Decision Theory and Bayesian Analysis
Berger, J. O. (1985) · Springer Series in Statistics · doi:10.1007/978-1-4757-4286-2 · Cited by 7,722
Self-citationApplied Statistical Decision Theory
Lindley, D. V., Raiffa, H., & Schlaifer, R. (1962) · Econometrica · doi:10.2307/1909900 · Cited by 2,183
An Introduction to Bayesian Inference in Econometrics.
Sargan, J. D., & Zellner, A. (1974) · Journal of the Royal Statistical Society Series A (General) · doi:10.2307/2344559 · Cited by 1,938
Bayesian statistical inference for psychological research.
Edwards, W., Lindman, H., & Savage, L. J. (1963) · Psychological Review · doi:10.1037/h0044139 · Cited by 1,734
Specification Searches: Ad Hoc Inference with Non-Experimental Data.
Richard, J.-F., & Leamer, E. E. (1981) · Economica · doi:10.2307/2553491 · Cited by 1,127
Lindley, D. V. (1957) · Biometrika · doi:10.1093/biomet/44.1-2.187 · Cited by 994
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
Self-citationProbability and the Weighing of Evidence.
M., P. A., & Good, I. J. (1950) · Journal of the Royal Statistical Society Series A (General) · doi:10.2307/2981046 · Cited by 753
Berger, J. O., & Wolpert, R. L. (1988) · doi:10.1214/lnms/1215466210 · Cited by 616
Self-citationThe Estimation of Probabilities: An Essay on Modern Bayesian Methods
Sparks, J. N., & Good, I. J. (1967) · American Educational Research Journal · doi:10.2307/1162122 · Cited by 533
Flagged first, then by citations
Research works printed but not linked: 8
- BERGER, J. (1986). Are P-values reasonable measures of accuracy. In Pacific Statistical Congress (I. S. Francis et al., eds.). NorthHolland, Amsterdam.
- DEMPSTER, A. P. (1971). Model searching and estimation in the logic of inference. In Foundations of Statistical Inference (V. P. Godambe and D. A. Sprott, eds.). Holt, Rinehart, and Winston, Toronto.
- DEMPSTER, A. P. (1973). The direct use of likelihood for significance testing. In Proc. of the Conference on Foundational Questions in Statistical Inference (O. Barndorff-Nielsen et al., eds.) 335–352. Dept. Theoretical Statistics, Univ. Aarhus.
- DICKEY, J. M. (1974). Bayesian alternatives to the F test and least squares estimate in the linear model. In Studies in Bayesian Econometrics and Statistics (S. E. Fienberg and A. Zellner, eds.) 515–554. North-Holland, Amsterdam.
- DIcKEY, J. M. (1980). Approximate coherence for regression model inference— with a new analysis of Fisher's Broadback Wheatfield example. In Bayesian Analysis in Econometrics and Statistics: Essays in Honor of Harold Jeffreys (A. Zellner, ed.) 333-354. North-Holland, Amsterdam.
- GooD, I. J. (1985). Weight of evidence: A brief survey. In Bayesian Statistics 2 (J. M. Bernardo, M. H. DeGroot, D. V. Lindley and A. F. M. Smith, eds.) 249–270. North-Holland, Amsterdam.
- JEFFREYs, H. (1957). Scientific Inference. Cambridge Univ. Press, Cambridge.
- JEFFREYs, H. (1961). Theory of Probability, 3rd ed. Oxford Univ. Press, London.
Cited by
Sorted by citations
Items 301–400 of 662
Kelter, R. (2021) · Computational Brain & Behavior · doi:10.1007/s42113-021-00110-5 · Cited by 14
Using BayesianP-Values in a 2 × 2 Table of Matched Pairs with Incompletely Classified Data
Lin, Y., Lipsitz, S. R., Sinha, D., Gawande, A. A., Regenbogen, S. E., & Greenberg, C. C. (2009) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1111/j.1467-9876.2008.00645.x · Cited by 14
Model Comparison of Bayesian Semiparametric and Parametric Structural Equation Models
Song, X.-Y., Xia, Y., Pan, J., & Lee, S. (2011) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705511.2011.532720 · Cited by 14
Assessing environmentally significant effects: a better strength-of-evidence than a single P value?
McBride, G. B., Cole, R. G., Westbrooke, I., & Jowett, I. G. (2013) · Environmental Monitoring and Assessment · doi:10.1007/s10661-013-3574-8 · Cited by 14
The posterior predictive p-value for the problem of goodness of fit
de la Horra, J., & Bernal, M. T. R. (1999) · Test · doi:10.1007/bf02595865 · Cited by 14
Testing Option Pricing Models with Stochastic Volatility, Random Jumps and Stochastic Interest Rates
Jiang, G. J. (2002) · International Review of Finance · doi:10.1111/j.1369-412x.2002.00040.x · Cited by 14
van der Sluis, P. J. (1997) · Studies in Nonlinear Dynamics and Econometrics · doi:10.2202/1558-3708.1031 · Cited by 14
Sensitivity of the Astrometric Technique in Detecting Outer PlanetsPreprint
Eisner, J. A., & Kulkarni, S. R. (2001) · Cited by 14
On intrinsic priors for nonnested models
Cano, J. A., Kessler, M., & Moreno, E. (2004) · Test · doi:10.1007/bf02595781 · Cited by 13
Bayesian model selection for unit root testing with multiple structural breaks
Vosseler, A. (2014) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2014.08.016 · Cited by 13
Berger, J. O., & Pericchi, L. R. (2015) · Wiley StatsRef: Statistics Reference Online · doi:10.1002/9781118445112.stat00224.pub2 · Cited by 13
Self-citationWorked-out examples of the adequacy of Bayesian optional stopping
Tendeiro, J. N., Kiers, H. A. L., & van Ravenzwaaij, D. (2021) · Psychonomic Bulletin & Review · doi:10.3758/s13423-021-01962-5 · Cited by 12
Tsao, C. A. (2006) · Test · doi:10.1007/bf02595421 · Cited by 12
A Comparison of Testing Methodologies
Berger, J. O. (2008) · CERN Document Server (European Organization for Nuclear Research) · doi:10.5170/cern-2008-001.8 · Cited by 12
Self-citationGönen, M., Johnson, W. O., Lu, Y., & Westfall, P. H. (2017) · The American Statistician · doi:10.1080/00031305.2017.1322142 · Cited by 12
Baseball players with the initial “K” do not strike out more often
McCullough, B. D., & McWilliams, T. P. (2010) · Journal of Applied Statistics · doi:10.1080/02664760902889965 · Cited by 12
Steps to implement Bayesian input distribution selection
Chick, S. E. (2003) · Winter Simulation Conference · doi:10.1109/wsc.1999.823090 · Cited by 12
A Bayesian alternative to parametric hypothesis testing
Rueda, R. (1992) · Test · doi:10.1007/bf02562662 · Cited by 12
Groundwater detection monitoring using combined information from multiple constituents
Small, M. J. (1997) · Water Resources Research · doi:10.1029/96wr03977 · Cited by 12
Multiple Bayes Factors for Testing Hypotheses
Bertolino, F., Piccinato, L., & Racugno, W. (1995) · Journal of the American Statistical Association · doi:10.1080/01621459.1995.10476504 · Cited by 12
Lindley, D. V. (2014) · Wiley StatsRef: Statistics Reference Online · doi:10.1002/9781118445112.stat05859 · Cited by 12
Testing Conditional Independence in Psychometric Networks: An Analysis of Three Bayesian MethodsPreprint
Sekulovski, N., Keetelaar, S., Huth, K. B. S., Wagenmakers, E., van Bork, R., van den Bergh, D., & Marsman, M. (2023) · doi:10.31234/osf.io/ch7a2 · Cited by 12
Zaslavsky, B. G. (2010) · Journal of Biopharmaceutical Statistics · doi:10.1080/10543401003619023 · Cited by 11
Power priors for replication studies
Pawel, S., Aust, F., Held, L., & Wagenmakers, E. (2023) · Test · doi:10.1007/s11749-023-00888-5 · Cited by 11
Comparing Conditional Quantile Curves
Dette, H., Wagener, J., & Stanislav Volgushev, A. (2010) · Scandinavian Journal of Statistics · doi:10.1111/j.1467-9469.2010.00718.x · Cited by 11
Two Bayesian tests of the GLOMOsys Model.
Field, S. M., Wagenmakers, E., Newell, B. R., Zeelenberg, R., & van Ravenzwaaij, D. (2016) · Journal of Experimental Psychology General · doi:10.1037/xge0000067 · Cited by 11
Tijmstra, J. (2018) · Psychonomic Bulletin & Review · doi:10.3758/s13423-018-1447-4 · Cited by 11
Comparing latent inequality with ordinal data
Kaplan, D. M., & Zhao, W. (2022) · Econometrics Journal · doi:10.1093/ectj/utac030 · Cited by 11
Measuring and Controlling Bias for Some Bayesian Inferences and the Relation to Frequentist Criteria
Evans, M. J., & Guo, Y. (2021) · Entropy · doi:10.3390/e23020190 · Cited by 12
A Cautionary Note on Estimating Effect Size
van den Bergh, D., Haaf, J. M., Ly, A., Rouder, J. N., & Wagenmakers, E. (2021) · Advances in Methods and Practices in Psychological Science · doi:10.1177/2515245921992035 · Cited by 11
Comparing spectral densities of stationary time series with unequal sample sizes
Preuß, P., & Hildebrandt, T. (2013) · Statistics & Probability Letters · doi:10.1016/j.spl.2013.01.015 · Cited by 11
Hilgard, J., Engelhardt, C. R., Bartholow, B. D., & Rouder, J. N. (2015) · Psychology of Popular Media Culture · doi:10.1037/ppm0000102 · Cited by 11
Encompassing Prior Based Model Selection for Inequality Constrained Analysis of Variance
Klugkist, I. G. (2008) · Springer eBooks · doi:10.1007/978-0-387-09612-4_4 · Cited by 12
Robust Bayesian displays for standard inferences concerning a normal mean
Fan, T., & Berger, J. O. (2000) · Computational Statistics & Data Analysis · doi:10.1016/s0167-9473(99)00060-2 · Cited by 11
Self-citationBridging the gap between statistical analysis and decision making in public health research
Stangl, D. K. (2005) · Statistics in Medicine · doi:10.1002/sim.2031 · Cited by 11
Statistical models for responses and response times
Entink, R. K. (2008) · doi:10.3990/1.9789036527552 · Cited by 11
Reply to Robert et al.: Model criticism informs model choice and model comparison
Ratmann, O., Andrieu, C., Wiuf, C., & Richardson, S. T. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0912887107 · Cited by 11
Lowe, M. X., Kettner, H., Jolly, D. R. P., Carhart-Harris, R. L., & Jackson, H. (2024) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2024.1279887 · Cited by 14
Sturrock, P. A., & Scargle, J. D. (2009) · The Astrophysical Journal · doi:10.1088/0004-637x/706/1/393 · Cited by 10
Restricted most powerful Bayesian tests for linear models
Goddard, S. D., & Johnson, V. E. (2016) · Scandinavian Journal of Statistics · doi:10.1111/sjos.12235 · Cited by 10
Identifying Alert Concentrations Using a Model-Based Bootstrap Approach
Möllenhoff, K., Schorning, K., & Kappenberg, F. (2022) · Biometrics · doi:10.1111/biom.13799 · Cited by 10
Role of P-values and other Measures of Evidence in Bayesian Analysis
Ghosh, J. K., Purkayastha, S., & Samanta, T. (2005) · Handbook of statistics · doi:10.1016/s0169-7161(05)25005-8 · Cited by 10
Robust Bayesian hypothesis testing in the presence of nuisance parameters
Berger, J. O., & Mortera, J. (1994) · Journal of Statistical Planning and Inference · doi:10.1016/0378-3758(94)90131-7 · Cited by 10
Self-citationInput uncertainty: accounting for parameter uncertainty in simulation input modeling
Zouaoui, F., & Wilson, J. R. (2001) · Winter Simulation Conference · doi:10.5555/564124.564175 · Cited by 10
Bayesian inference in numerical cognition: A tutorial using JASPPreprint
Faulkenberry, T. J., Ly, A., & Wagenmakers, E. (2020) · doi:10.31234/osf.io/vg9pw · Cited by 10
Wester, D. B. (2018) · Biometrics & Biostatistics International Journal · doi:10.15406/bbij.2018.07.00192 · Cited by 9
Comparison of Some Methods of Testing Statistical Hypotheses: (Part I. Parallel Methods)
Parada, J. E. L., García, A. P., Galeano, L. K. L., & Monsalve, P. A. G. (2014) · Mundo FESC · doi:10.6000/1929-6029.2014.03.02.11 · Cited by 9
Bayesian and frequentist approaches to resonance searches
Fowlie, A. (2019) · Journal of Instrumentation · doi:10.1088/1748-0221/14/10/p10031 · Cited by 9
Saleh, R. A., Islam, M. T., & Nor, R. N. H. (2022) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-19-2347-0_54 · Cited by 9
An Evaluation of Bayesian Inequality Constrained Analysis of Variance
Hoijtink, H., Huntjens, R. J. C., Reijntjes, A. H. A., Kuiper, R. M., & Boelen, P. A. (2008) · Springer eBooks · doi:10.1007/978-0-387-09612-4_5 · Cited by 9
Comparative Classical and Bayesian Interpretations of Statistical Compliance Tests in Auditing
Johnstone, D. J. (1997) · Accounting and Business Research · doi:10.1080/00014788.1997.9728899 · Cited by 9
Noninformative Bayesian testing and neutral Bayes factors
Robert, C. P., & Caron, N. (1996) · Test · doi:10.1007/bf02562626 · Cited by 9
Statistical hypothesis testing by weak-value amplification: Proposal and evaluation
Susa, Y., & Tanaka, S. (2015) · Physical Review A · doi:10.1103/physreva.92.012112 · Cited by 11
Should significance testing be abandoned in machine learning?
Berrar, D., & Dubitzky, W. (2018) · International Journal of Data Science and Analytics · doi:10.1007/s41060-018-0148-4 · Cited by 9
Lower bounds on Bayes factors for invariant testing situations
Delampady, M. (1989) · Journal of Multivariate Analysis · doi:10.1016/0047-259x(89)90107-3 · Cited by 9
Self-citationMoran, J. L., & Solomon, P. J. (2004) · Critical Care and Resuscitation · doi:10.1016/s1441-2772(23)01858-6 · Cited by 9
Closed-Form Power and Sample Size Calculations for Bayes Factors
Pawel, S., & Held, L. (2025) · The American Statistician · doi:10.1080/00031305.2025.2467919 · Cited by 16
Four Requirements for an Acceptable Research Program
Marsman, M., Ly, A., & Wagenmakers, E. (2016) · Basic and Applied Social Psychology · doi:10.1080/01973533.2016.1221349 · Cited by 8
A general approximation to nested Bayes factors with informed priors
Bartos̆, F., & Wagenmakers, E. (2023) · Stat · doi:10.1002/sta4.600 · Cited by 12
A local unit root test in mean for financial time series
Chen, C. W. S., & Lee, S. (2015) · Journal of Statistical Computation and Simulation · doi:10.1080/00949655.2015.1037765 · Cited by 8
Evidential Calibration of Confidence Intervals
Pawel, S., Ly, A., & Wagenmakers, E. (2023) · The American Statistician · doi:10.1080/00031305.2023.2216239 · Cited by 8
Fan, T., & Berger, J. O. (1990) · Journal of Statistical Computation and Simulation · doi:10.1080/00949659008811284 · Cited by 8
Self-citationNHST is still logically flawed
Schneider, J. (2018) · Scientometrics · doi:10.1007/s11192-018-2655-4 · Cited by 10
Modified p-values for one-sided testing in restricted parameter spaces
Wang, H. (2006) · Statistics & Probability Letters · doi:10.1016/j.spl.2006.09.010 · Cited by 8
A Weighted Nonparametric Procedure for the Combination of Independent Events
Koziol, J. A., & Tuckwell, H. C. (1994) · Biometrical Journal · doi:10.1002/bimj.4710360814 · Cited by 8
LaValle, S. M., Moroney, K. J., & Hutchinson, S. (1997) · IEEE Transactions on Image Processing · doi:10.1109/83.650119 · Cited by 8
P Values: What They Are and How to Use Them
Demortier, L. (2007) · Cited by 8
History, Ethics, and Intensification in Agriculture
Perkins, J. H., & Jamison, R. (2008) · The international library of environmental, agricultural and food ethics/The International library of environmental, agricultural and food ethics · doi:10.1007/978-1-4020-8722-6_3 · Cited by 8
Two-sample test for sparse high-dimensional multinomial distributions
Plunkett, A., & Park, J. (2018) · Test · doi:10.1007/s11749-018-0600-8 · Cited by 7
Conflicts in Bayesian Statistics Between Inference Based on Credible Intervals and Bayes Factors
Lovric, M. M. (2020) · Journal of Modern Applied Statistical Methods · doi:10.22237/jmasm/1556670540 · Cited by 13
Bayesian modelling of integer-valued transfer function models
Pingal, A. C., & Chen, C. W. S. (2022) · Statistical Modelling · doi:10.1177/1471082x221075477 · Cited by 7
Model equivalence tests in a parametric framework
Lavergne, P. (2013) · Journal of Econometrics · doi:10.1016/j.jeconom.2013.05.007 · Cited by 7
Testing Un-Separated Hypotheses by Estimating a Distance
Salomond, J.-B. (2017) · Bayesian Analysis · doi:10.1214/17-ba1059 · Cited by 7
On the Jeffreys-Lindley Paradox and the Looming Reproducibility Crisis in Machine Learning
Berrar, D., & Dubitzky, W. (2017) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa.2017.3 · Cited by 7
Confidence and varieties of bias
Heathcote, A., Holloway, E., & Sauer, J. D. (2018) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2018.10.002 · Cited by 7
A Bayesian method for combining statistical tests
Koziol, J. A., & Tuckwell, H. C. (1999) · Journal of Statistical Planning and Inference · doi:10.1016/s0378-3758(98)00222-5 · Cited by 7
Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (2010) · Statistics, a series of textbooks and monographs · doi:10.1201/ebk1439836149-c9 · Cited by 8
Bayesian Isotonic Regression for Discrete Outcomes
Dunson, D. B. (2003) · Cited by 7
Levine, R. A., & Casella, G. (1996) · Journal of Statistical Planning and Inference · doi:10.1016/s0378-3758(95)00198-0 · Cited by 7
Bayesian inference and testing any hypothesis you can specifyPreprint
Etz, A. J., Haaf, J. M., Rouder, J. N., & Vandekerckhove, J. (2018) · doi:10.31234/osf.io/wmf3r · Cited by 16
Statistical inference for function-on-function linear regression
Dette, H., & Tang, J. (2023) · Bernoulli · doi:10.3150/23-bej1598 · Cited by 6
What Is In a (Non‐) Significant Finding? Moving Beyond False Dichotomies
Steenbergen, M. R. (2019) · Swiss Political Science Review · doi:10.1111/spsr.12373 · Cited by 6
Bayesian hypothesis testing and estimation under the marginalized random-effects meta-analysis model
van Aert, R. C. M., & Mulder, J. (2021) · Psychonomic Bulletin & Review · doi:10.3758/s13423-021-01918-9 · Cited by 7
Automatic Bayes factors for testing variances of two independent normal distributions
Böing-Messing, F., & Mulder, J. (2015) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2015.08.001 · Cited by 6
A new Bayesian procedure for testing point null hypotheses
Yin, Y. (2011) · Computational Statistics · doi:10.1007/s00180-011-0252-6 · Cited by 6
Smith, I., & Ferrari, A. (2014) · Bayesian Analysis · doi:10.1214/14-ba877 · Cited by 6
Comments on: An updated review of Goodness-of-Fit tests for regression models
Sperlich, S. (2013) · Test · doi:10.1007/s11749-013-0330-x · Cited by 6
Caveats and pitfalls in crowdsourcing research: the case of soccer referee bias
Berrar, D., Lopes, P., & Dubitzky, W. (2017) · International Journal of Data Science and Analytics · doi:10.1007/s41060-017-0057-y · Cited by 6
de la Horra, J. (2005) · Communication in Statistics- Theory and Methods · doi:10.1081/sta-200052129 · Cited by 6
Oh, M. (1998) · Communication in Statistics- Theory and Methods · doi:10.1080/03610929808832233 · Cited by 6
A fair comparison between regression models of different dimensionPreprint
Vos, A. F. D. (1993) · RePEc: Research Papers in Economics · Cited by 6
Comparing hierarchical models using Bayes factor and fractional Bayes factor: a robust analysis
Santis, F. D., & Spezzaferri, F. (1996) · Lecture notes-monograph series · doi:10.1214/lnms/1215453075 · Cited by 6
A Simple Goodness-of-fit Test for Linear Models Under a Random Design Assumption
Dette, H., & Munk, A. (1998) · Annals of the Institute of Statistical Mathematics · doi:10.1023/a:1003439114929 · Cited by 6
Press, S. J. (2002) · Wiley series in probability and statistics · doi:10.1002/9780470317105.ch9 · Cited by 6
Lovrić, M. (2020) · Journal of Modern Applied Statistical Methods · doi:10.22237/jmasm/1556670180 · Cited by 5
Linde, M., & van Ravenzwaaij, D. (2023) · BMC Medical Research Methodology · doi:10.1186/s12874-023-02097-y · Cited by 7
A Bayesian Analysis for the Homogeneity Testing Problem Using ϵ–Contaminated Priors
Gómez-Villegas, M. A., & González‐Pérez, B. (2011) · Communication in Statistics- Theory and Methods · doi:10.1080/03610920903520547 · Cited by 5
Default Bayes Factors for Testing the (In)equality of Several Population Variances
Dablander, F., van den Bergh, D., Wagenmakers, E., & Ly, A. (2023) · Bayesian Analysis · doi:10.1214/23-ba1369 · Cited by 5
Carmichael, J. D. (2021) · Geophysical Journal International · doi:10.1093/gji/ggab055 · Cited by 5
Bayesian tests of symmetry for the generalized Von Mises distribution
Salvador, S., & Gatto, R. (2021) · Computational Statistics · doi:10.1007/s00180-021-01147-7 · Cited by 5
Metadata
- Authors
- Berger, J. O., & Delampady, M.
- Venue
- Statistical Science
- Publisher
- Institute of Mathematical Statistics
- Published
- 1987-08-01
- Volume
- 2
- Issue
- 3
- Pages
- 317-335
- ISSN
- 0883-4237 (ISSN-L)
- 2168-8745
- OpenAlex
- W2128585330
- Cited by
- 664
- References
- 35
- Citations by year
- 2025: 6 · 2024: 11 · 2023: 14 · 2022: 20 · 2021: 29 · 2020: 24 · 2019: 29 · 2018: 26 · 2017: 22 · 2016: 32 · 2015: 23 · 2014: 21 · 2013: 23 · 2012: 22
- FWCI
- 10.67
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Statistical Science (publishedVersion)
- Project Euclid (Cornell University) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Keywords
- point null hypothesis, p-values, Bayes factor, posterior probability, normal distribution, binomial distribution, objective testing, confidence regions
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