Bayesian versus orthodox statistics: Which side are you on?

Perspectives on Psychological Science

Volumn 6, Issue 3, 2011, Pages 274-290

  Dienes, Zoltan ^a  

^a UNIVERSITY OF SUSSEX   (United Kingdom)

Author keywords

Bayes; Evidence; Likelihood principle; Significance testing; Statistical inference

Indexed keywords

EID: 80051815956     PISSN: 17456916     EISSN: 17456924     Source Type: Journal    
DOI: 10.1177/1745691611406920     Document Type: Article

References (49)

1. Armitage, P., Berry, G., & Matthews, J.N.S. (2002). Statistical methods in medical research (4th ed.). Malden, MA: Blackwell.
2. Armitage, P., McPherson, C.K., & Rowe, B.C. (1969). Repeated significance tests on accumulating data. Journal of the Royal Statistical Society: Series A. Statistics in Society, 132, 235-244.
3. Baguley, T. (2009). Standardized or simple effect size: What should be reported? British Journal of Psychology, 100, 603-617.
4. Baguley, T. (in press). Beyond analysis of variance. Hampshire, England: Palgrave Macmillian.
5. Baguley, T., & Kaye, W.S. (2010). Review of "Understanding Psychology as a Science: An Introduction to Scientific and Statistical Inference". British Journal of Mathematical & Statistical Psychology, 63, 695-698.
6. Bargh, J.A., Chen, M., & Burrows, L. (1996). Automaticity of social behavior: Direct effects of trait construct and stereotype activation on action. Journal of Personality and Social Psychology, 71, 230-244.
7. Berger, J. (2003). Could Fisher, Jeffreys and Neyman have agreed on testing? Statistical Science, 18, 1-32.
8. Birnbaum, A. (1962). On the foundations of statistical inference. Journal of the American Statistical Association, 53, 259-326.
9. Blume, J. D. (in press). Likelihood and its evidential framework. In P.S. Bandyopadhyay & M. Forster (Eds.), Handbook of the philosophy of statistics. Amsterdam: Elsevier.
10. Coulson, M., Healey, M., Fidler, F., & Cumming, G. (2010). Confidence intervals permit, but do not guarantee, better inference than statistical significance testing. Frontiers in Psychology, 1, article 26. Retrieved from http://www.frontiersin.org/quantitative_psychology_and_measurement/10.3389/fpsyg.2010.00026/full
11. Cox, R.T. (1946). Probability, frequency, and reasonable expectation. American Journal of Physics, 14, 1-13.
12. Dienes, Z. (2008). Understanding psychology as a science: An introduction to scientific and statistical inference. Hampshire, England: Palgrave Macmillan. Retrieved from http://www.lifesci.sussex.ac.uk/home/Zoltan_Dienes/inference/
13. Dienes, Z. (2010). Measuring the conscious status of knowledge. In L.A.P. Miranda & A.I. Madariaga (Eds.), Advances in cognitive science: Learning, evolution and social action (pp. 113-128). Bilbao, Spain: University of the Basque Country Press.
14. Fidler, F., Thomason, N., Cumming, G., Finch, S., & Leeman, J. (2004). Editors can lead researchers to confidence intervals, but can't make them think. Psychological Science, 15, 119-126.
15. Gigerenzer, G. (2004). Mindless statistics. Journal of Socio-Economics, 33, 587-606.
16. Halpern, J.Y. (1999). Cox's theorem revisited. Journal of Artificial Intelligence Research, 11, 429-435.
17. Harlow, L.L., Mulaik, S.A., Steiger, J.H. (Eds.). (1997). What if there were no significance tests? Mahwah, NJ: Erlbaum.
18. Hoijtink, H., Klugkist, I., & Boelen, P.A. (Eds.). (2008). Bayesian evaluation of informative hypotheses. New York: Springer.
19. Howard, G.S., Maxwell, S.E., & Fleming, K.J. (2000). The proof of the pudding: An illustration of the relative strengths of null hypothesis, meta-analysis, and Bayesian analysis. Psychological Methods, 5, 315-332.
20. Howson, C., & Urbach, P. (2006). Scientific reasoning: The Bayesian approach (3rd ed.). Chicago: Open Court.
21. Jaynes, E.T. (2003). Probability theory: The logic of science. Cambridge, England: Cambridge University Press.
22. Jeffreys, H. (1961). The theory of probability (3rd ed.). Oxford, England: Oxford University Press.
23. Johansson, T. (in press). Hail the impossible: p values, evidence, and likelihood. Scandinavian Journal of Psychology.
24. Kerr, N.L. (1998). HARKing: Hypothesizing after the results are known. Personality and Social Psychology Review, 2, 196-217.
25. Kruschke, J.K. (2010a). Bayesian data analysis. Wiley Interdisciplinary Reviews: Cognitive Science, 1, 658-676.
26. Kruschke, J.K. (2010b). Doing Bayesian data analysis: A tutorial with R and BUGS. Burlington, MA: Academic Press.
27. Kruschke, J.K. (2010c). What to believe: Bayesian methods for data analysis. Trends in Cognitive Sciences, 14, 293-300.
28. Kruschke, J.K. (2011). Bayesian assessment of null values via parameter estimation and model comparison. Perspectives on Psychological Science, 6, 299-312.
29. Lakatos, I. (1978). The methodology of scientific research programmes: Philosophical papers (Vol. 1). Cambridge, England: Cambridge University Press.
30. Lakatos, I., & Feyerabend, P. (1999). For and against method. Chicago: University of Chicago Press.
31. Mayo, D. (1996). Error and the growth of experimental knowledge. Chicago: University of Chicago Press.
32. Mayo, D.G. (2004). An error statistical philosophy of evidence. In M.L. Taper & S.R. Lele (Eds.), The nature of scientific evidence: Statistical, philosophical and empirical considerations (pp. 79-96). Chicago: University of Chicago Press.
33. Meehl, P. (1967). Theory-testing in psychology and physics: A methodological paradox. Philosophy of Science, 34, 103-115.
34. Miller, D. (1994). Critical rationalism: A restatement and defence. Chicago: Open Court.
35. Mussweiler, T. (2006). Doing is for thinking! Psychological Science, 17, 17-21.
36. Oakes, M. (1986). Statistical inference: A commentary for the social and behavioural sciences. Hoboken, NJ: Wiley.
37. Popper, K. (1963). Conjectures and refutations. London: Routledge.
38. Rouder, J.N., Morey, R.D., Speckman, P.L., & Pratte, M.S. (2007). Detecting chance: A solution to the null sensitivity problem in subliminal priming. Psychonomic Bulletin & Review, 14, 597-605.
39. Rouder, J.N., Speckman, P.L., Sun, D., Morey, R.D., & Iverson, G. (2009). Bayesian t tests for accepting and rejecting the null hypothesis. Psychonomic Bulletin & Review, 16, 225-237.
40. Royall, R.M. (1997). Statistical evidence: A likelihood paradigm. London: Chapman & Hall.
41. Savage, L.J. (1962). The foundations of statistical inference: A discussion. London: Methuen.
42. Sivia, D.S., & Skilling, J. (2006). Data analysis: A Bayesian tutorial (2nd ed.). Oxford, England: Oxford University Press.
43. Taper, M.L., & Lele, S.R. (2004). The nature of scientific evidence: Statistical, philosophical and empirical considerations. Chicago: University of Chicago Press.
44. von Mises, R. (1957). Probability, statistics and truth. London: Macmillan.
45. Wagenmakers, E.J. (2007). A practical solution to the pervasive problems of p values.Psychonomic Bulletin & Review, 14, 779-804.
46. Westfall, P.H., Johnson, W.O., & Utts, J.M. (1997). A Bayesian perspective on the Bonferroni adjustment. Biometrika, 84, 419-427.
47. Wetzels, R., Matzke, D., Lee, M.D., Rouder, J.N., Iverson, G.J., & Wagenmakers, E.J. (2011). Statistical evidence in experimental psychology: An empirical comparison using 855 t tests. Perspectives on Psychological Science, 6, 291-298.
48. Wright, D.B. (2010). Ten statisticians and their impacts for psychologists. Perspectives on Psychological Science, 4, 587-597.
49. Ziliak, S.T., & McCloskey, D.N. (2008). The cult of statistical significance: How the standard error cost us jobs, justice and lives. Ann Arbor: University of Michigan Press.