Wagenmakers, EJ. A practical solution to the pervasive problems of p values. Psychon Bull Rev 2007, 14: 779–804.

Sadiku, MNO, Tofighi, MR. A tutorial on simulation of queuing models. Int J Elec Eng Educ 1999, 36: 102–120.

Thompson, B. What future quantitative social science research could look like: confidence intervals for effect sizes. Educ Researcher 2002, 31: 25–32.

Cumming, G, Finch, S. A primer on the understanding, use and calculation of confidence intervals based on central and noncentral distributions. Educ Psychol Meas 2001, 61: 530–572.

Young, KD, Lewis, RJ. What is confidence? Part 2: detailed definition and determination of confidence intervals. Ann Emerg Med 1997, 30: 311–318.

Miller, J. What is the probability of replicating a statistically significant effect? Psychon Bull Rev 2009, 16: 617–640.

Doyle, AC. The Sign of Four. London: Spencer Blackett; 1890.

Anderson, JR. The Adaptive Character of Thought. Hillsdale, NJ: Lawrence Erlbaum Associates; 1990.

Chater, N, Oaksford, M, eds. The probabilistic mind. Oxford: Oxford University Press; 2008.

Chater, N, Tenenbaum, JB, Yuille, A. Special issue: probabilistic models of cognition. Trends Cogn Sci 2006, 10: 287–344.

Kruschke, JK. Bayesian approaches to associative learning: from passive to active learning. Learn Behav 2008, 36: 210–226.

Lee, MD. Three case studies in the Bayesian analysis of cognitive models. Psychon Bull Rev 2008, 15: 1–15.

Lee, MD, Wagenmakers, EJ. Bayesian statistical inference in psychology: Comment on Trafimow (2003). Psychol Rev 2005, 112: 662–668.

Kruschke, JK. Highlighting a canonical experiment. In: Ross, B, ed. The Psychology of Learning and Motivation: Academic Press; 2009; 153–185.

Lamberts, K, Kent, C. No evidence for rule‐based processing in the inverse base‐rate effect. Mem Cognit 2007, 35: 2097–2105.

Kruschke, JK. Base rates in category learning. J Exp Psychol Learn Mem Cogn 1996, 22: 3–26.

Gelman, A. Analysis of variance—why it is more important than ever. Ann Stat 2005, 33: 1–53.

Gelman, A, Hill, J, Yajima, M. Why we (usually) don`t have to worry about multiple comparisons. 2009. http://www.stat.columbia.edu/∼gelman/research/unpublished/multiple2.pdf. Accessed March 2009.

Qian, SS, Shen, Z. Ecological applications of multilevel analysis of variance. Ecology 2007, 88: 2489–2495.

Rouder, JN, Lu, J, Speckman, P, Sun, D, Jiang, Y. A hierarchical model for estimating response time distributions. Psychon Bull Rev 2005, 12: 195–223.

Berry, DA, Hochberg, Y. Bayesian perspectives on multiple comparisons. J Stat Plan Inf 1999, 82: 215–227.

Lindquist, MA, Gelman, A. Correlations and multiple comparisons in functional imaging—a statistical perspective. Pers Psychol Sci 2009, 4: 310–313.

Meng, CYK, Dempster, AP. A Bayesian approach to the multiplicity problem for significance testing with binomial data. Biometrics 1987, 43: 301–311.

Berry, DA. Statistics: A Bayesian Perspective. Belmont, CA: Duxbury Press/Wadsworth; 1996.

Bolstad, WM. Introduction to Bayesian Statistics. 2nd ed. Hoboken, NJ: John Wiley %26 Sons; 2007.

Carlin, BP, Louis, TA. Bayesian Methods for Data Analysis. 3rd ed. Boca Raton, FL: CRC Press; 2009.

Gelman, A, Carlin, JB, Stern, HS, Rubin, DB. Bayesian Data Analysis. 2nd ed. Boca Raton, FL: CRC Press; 2004.

Gelman, A, Hill, J. Data Analysis using Regression and Multilevel/Hierarchical Models. New York: Cambridge University Press; 2007.

Lynch, SM. Introduction to Applied Bayesian Statistics and Estimation for Social Scientists. New York: Springer; 2007.

Freedman, LS, Lowe, D, Macaskill, P. Stopping rules for clinical trials incorporating clinical opinion. Biometrics 1984, 40: 575–586.

Spiegelhalter, DJ, Freedman, LS, Parmar, MKB. Bayesian approaches to randomized trials. J R Stat Soc [Ser A] 1994, 157: 357–416.

Hobbs, BP, Carlin, BP. Practical Bayesian design and analysis for drug and device clinical trials. J Biopharm Stat 2008, 18: 54–80.

Edwards, W, Lindman, H, Savage, LJ. Bayesian statistical inference for psychological research. Psychol Rev 1963, 70: 193–242.

Rouder, JN, Speckman, PL, Sun, D, Morey, RD, Iverson, G. Bayesian t‐tests for accepting and rejecting the hypothesis. Psychon Bull Rev 2009, 16: 225–237.

Gallistel, CR. The importance of proving the . Psychol Rev 2009, 116: 439–453.

Solari, F, Liseo, B, Sun, D. Some remarks on Bayesian inference for one‐way ANOVA models. Ann Inst Stat Math 2008, 60: 483–498.

Mueller, P, Parmigiani, G, Rice, K. FDR and Bayesian multiple comparisons rules. In: Bernardo, JM, Bayarri, MJ, Berger, JO, David, AP, Heckerman, D, *et al.*, eds. Bayesian Statistics 8. Oxford: Oxford University Press; 2007.

Scott, JG, Berger, JO. An exploration of aspects of Bayesian multiple testing. J Stat Plan Inf 2006, 136: 2144–2162.

Gopalan, R, Berry, D. Bayesian multiple comparisons using Dirichlet process priors. J Am Stat Assoc, 1998, 93: 1130–1139.

Stone, M. Discussion of papers by Dempster and Aitken. Stat Comput 1997, 7: 263–264.

Liu, CC, Aitkin, M. Bayes factors: prior sensitivity and model generalizability. J Math Psychol 2008, 52: 362–375.

Jefferys, WH, Berger, JO. Ockham`s razor and Bayesian analysis. Am Sci 1992, 80: 64–72.

Kass, RE, Raftery, AE. Bayes factors. J Am Stat Assoc 1995, 90: 773–795.

Johnson, VE, Cook, JD. Bayesian Design of Single‐arm Phase II Clinical Trials with Continuous Monitoring. University of Texas, MD Anderson Cancer Center Department of Biostatistics Working Paper Series. The Berkeley Electronic Press. 2008; July. (Working Paper 47, http://www.bepress.com/mdandersonbiostat/paper47).

Kruschke, JK. Human category learning: implications for back propagation models. Connection Sci 1993, 5: 3–36.

Lee, MD, Webb, MR. Modeling individual differences in cognition. Psychon Bull Rev 2005, 12: 605–621.

Navarro, DJ, Griffiths, TL, Steyvers, M, Lee, MD. Modeling individual differences using Dirichlet processes. J Math Psychol 2006, 50: 101–122.

Adcock, CJ. Sample size determination: a review. The Statistician 1997, 46: 261–283.

De Santis, F. Statistical evidence and sample size determination for Bayesian hypothesis testing. J Stat Plan Inf 2004, 124: 121–144.

De Santis, F. Using historical data for Bayesian sample size determination. J R Stat Soc [Ser A] 2007, 170: 95–113.

Joseph, L, Wolfson, DB, du Berger, R. Sample size calculations for binomial proportions via highest posterior density intervals. The Statistician 1995, 44: 143–154.

Joseph, L, Wolfson, DB, du Berger, R. Some comments on Bayesian sample size determination. The Statistician 1995, 44: 167–171.

Wang, F, Gelfand, AE. A simulation‐based approach to Bayesian sample size determination for performance under a given model and for separating models. Stat Sci 2002, 17: 193–208.

Weiss, R. Bayesian sample size calculations for hypothesis testing. The Statistician 1997, 46: 185–191.

Jacobs, RA, Kruschke, JK. Bayesian learning theory applied to human cognition. Wiley Interdisciplinary Reviews: Cognitive Science 2010; (in press).

Kruschke, JK. Doing Bayesian data analysis: A tutorial introduction with R and BUGS. Academic Press; 2010.

Litton, CD, Buck, CE. The Bayesian approach to the interpretation of archaeological data. Archaeometry 1995, 37: 1–24.

Loredo, TJ. The promise of Bayesian inference for astrophysics. In: Feigelson, ED, Babu, GJ, eds. Statistical Challenges in Modern Astronomy. New York: Springer‐Verlag; 1992; 275–297.

Wade, PR. Bayesian methods in conservation biology. Conserv Biol 2000; 1308–1316.

Ellison, AM. Bayesian inference in ecology. Ecol Biol 2004, 7: 509–520.

Dunson, DB. Commentary: practical advantages of Bayesian analysis of epidemiologic data. Am J Epidemiol 2001, 153: 1222–1226.

Huelsenbeck, JP, Ronquist, F, Nielsen, R, Bollback, JP. Bayesian inference of phylogeny and its impact on evolutionary biology. Science 2001, 294: 2310–2314.

Berliner, LM, Royle, JA, Wikle, CK, Milliff, RF. Bayesian methods in the atmospheric sciences. In: Bernardo, JM, Berger, JO, Dawid, AP, Smith, AFM, eds. Bayesian Statistics 6: Proceedings of the sixth Valencia international meeting, June 6–10, 1998. Oxford, UK: Oxford University Press; 1999; 83–100.

Jackman, S. Bayesian analysis for political research. Ann Rev Political Sci 2004, 7: 483–505.

R Development Core Team (2008). R: A language and environment for statistical computing. R Foundation for statistical computing, Vienna, Austria. ISBN 3‐900051‐07‐0 URL http://www.R‐project.org.

Thomas, A. BRugs user manual (the R interface to BUGS). 2004. http://mathstat.helsinki.fi/openbugs/data/Docu/BRugs%20Manual.html. (Accessed 2010).

Thomas, A, O`Hara, B, Ligges, U, Sturtz, S. Making BUGS open. R News 2006, 6: 12–17.

Gilks, WR, Thomas, A, Spiegelhalter, DJ. A language and program for complex Bayesian modelling. The Statistician 1994, 43: 169–177.