Using epistemic ratios to evaluate hypotheses: An imprecision penalty for imprecise hypotheses

Genetic, Social, and General Psychology Monographs

Volumn 132, Issue 4, 2006, Pages 431-462

  Trafimow, David ^a  

^a NEW MEXICO STATE UNIVERSITY   (United States)

Author keywords

Epistemic estimates; Epistemic ratios; Epistemic updating; Likelihood ratio; Null hypothesis; Significance testing

Indexed keywords

ARTICLE; BAYES THEOREM; HUMAN; PROBABILITY; PSYCHOLOGICAL THEORY; PSYCHOLOGY; REPRODUCIBILITY; RESEARCH; STATISTICAL ANALYSIS; STATISTICS;

BAYES THEOREM; DATA INTERPRETATION, STATISTICAL; HUMANS; PROBABILITY; PSYCHOLOGICAL THEORY; PSYCHOLOGY; REPRODUCIBILITY OF RESULTS; RESEARCH;

EID: 41549106742     PISSN: 87567547     EISSN: None     Source Type: Journal    
DOI: 10.3200/MONO.132.4.431-462     Document Type: Article

References (57)

1. Abelson, R. P. (1997). A retrospective on the significance test ban of 1999 (If there were no significance tests, they would be invented). In L. Harlow, S. A. Mulaik, & J. H. Steiger (Eds.), What if there were no significance tests? (pp. 117-141). Mahwah, NJ: Erlbaum.
2. Bakan, D. (1966). The test of significance in psychological research. Psychological Bulletin, 66, 423-437.
3. Bradley, J. V. (1976). Probability; Decision; Statistics. Englewood Cliffs, NJ: Prentice-Hall.
4. Carver, R. P. (1978). The case against statistical significance testing. Harvard Educational Review, 48, 378-399.
5. Carver, R. P. (1993). The case against statistical significance testing, revisited. The Journal of Experimental Education, 61, 287-292.
6. Chihara, C. S. (1994). The Howson-Urbach proofs of Bayesian principles. In E. Eells & B. Skyrms (Eds.), Probability and conditionals: Belief revision and rational decision (pp. 161-178). New York: Cambridge University Press.
7. Chow, S. L. (1998). Precis of statistical significance: Rationale, validity, and utility. Behavioral and Brain Sciences, 21, 169-239.
8. Cohen, J. C. (1969). Statistical power analysis for the behavioral sciences. New York: Academic Press.
9. Cohen, J. (1994). The earth is round (p < .05). American Psychologist, 49, 997-1003.
10. Deutsch, D. (1997). The fabric of reality. New York: Penguin Books.
11. Edwards, A. W. F. (1992). Likelihood: Expanded edition. Baltimore: Johns Hopkins University Press.
12. Edwards, W., Lindman, H., & Savage, L. J. (1963). Bayesian statistical inference for psychological research. Psychological Review, 70, 193-242.
13. Eells, E. (1990). Bayesian problems of old evidence. In C. W. Savage (Ed.), Minnesota studies in the philosophy of science (Vol. 14, pp. 205-223). Minneapolis: University of Minnesota Press.
14. Fisher, R. A. (1925). Statistical methods for research workers. Edinburgh, Scotland: Oliver and Boyd.
15. Fisher, R. A. (1973). Statistical methods and scientific inference (3rd ed.). London: Collier Macmillan.
16. Foucault, M. (1970). The order of things. New York: Vintage.
17. Glymour, C. (1980). Theory and evidence. Princeton, NJ: Princeton University Press.
18. Hagen, R. L. (1997). In praise of the null hypothesis statistical test. American Psychologist. 52, 15-24.
19. Howson, C. (1990). Fitting your theory to the facts: Probably not such a bad thing after all. In C. W. Savage (Ed.), Minnesota studies in the philosophy of science (Vol. 14, pp. 224-44). Minneapolis: University of Minnesota Press.
20. Howson, C., & Urbach, P. (1989). Scientific reasoning: The Bayesian approach. La Salle, IL: Open Court.
21. Howson, C., & Urbach, P. (1994). Probability, uncertainty and the practice of statistics. In G. Wright and P. Ayton (Eds.), Subjective probability (pp. 39-51). Chichester, England: Wiley.
22. Kass, R. E., & Raftery, A. E. (1995). Bayes factors. Journal of the American Statistical Association, 90, 773-795.
23. Krueger, J. (2001). Null hypothesis significance testing: On the survival of a flawed method. American Psychologist, 56, 16-26.
24. Lakatos, I. (1970). Falsification and the methodology of scientific research programs. In I. Lakatos & A. Musgrave (Eds.), Criticism and the growth of knowledge (pp. 91-195). Cambridge, England: Cambridge University Press.
25. Lakatos, I. (1978). The methodology of scientific research programmes. Cambridge, England: Cambridge University Press.
26. Laplace, S. (1951). Essays on probabilities. New York: Dover.
27. Lederman, L. (1993). The God particle: If the universe is the answer, what is the question? New York: Houghton Mifflin.
28. McFadden, J. (2000). Quantum evolution. New York: Norton.
29. Meehl, P. E. (1978). Theoretical risks and tabular asterisks: Sir Karl, Sir Ronald, and the slow progress of soft psychology. Journal of Consulting and Clinical Psychology, 46, 806-834.
30. Meehl, P. E. (1990). Appraising and amending theories: The strategy of Lakatosian defense and two principles that warrant using it. Psychological Inquiry, 1, 108-141.
31. Meehl, P. E. (1997). The problem is epistemology, not statistics: Replace significance tests by confidence intervals and quantify accuracy of risky numerical predictions. In L. Harlow, S. A. Mulaik, & J. H. Steiger (Eds.), What if there were no significance tests? (pp. 393-425). Mahwah, NJ: Erlbaum.
32. Michelson, A. A., & Morley, E. W. (1887). On the relative motion of earth and luminiferous ether. American Journal of Science, Third Series, 34, 203, 233-245.
33. Mulaik, S. A., Raju, N. S., & Harshman, R. A. (1997). There is a time and place for significance testing. In L. Harlow, S. A. Mulaik, & J. H. Steiger (Eds.), What if there were no significance tests? (pp. 65-115). Mahwah, NJ: Erlbaum.
34. Nickerson, R. S. (2000). Null hypothesis significance testing: A review of an old and continuing controversy. Psychological Methods, 5, 241-301.
35. Nickerson, R. S. (2002). The production and perception of randomness. Psychological Review, 109, 330-337.
36. Phillips, L. D. (1973). Bayesian statistics for social scientists. New York: Thomas Y. Crowell.
37. Platt, J. R. (1964). Strong inference. Science, 146, 347-353.
38. Popper, K. R. (1959). The logic of scientific discovery. New York: Basic Books.
39. Popper, K. R. (1972). Objective knowledge. Oxford, England: Oxford University Press.
40. Popper, K. R. (1983). Realism and the aim of science. London: Routledge.
41. Quine, W. V. (1969). Ontological relativity and other essays. New York: Columbia University Press.
42. Reichardt, C. S., & Gollob, H. F. (1997). When confidence intervals should be used instead of statistical tests, and vice versa. In L. Harlow, S. A. Mulaik, & J. H. Steiger (Eds.), What if there were no significance tests? (pp. 259-284). Mahwah, NJ: Erlbaum.
43. Rindskopf, D. M. (1997). Testing "small," not null, hypotheses: Classical and Bayesian approaches. In L. Harlow, S. A. Mulaik, & J. H. Steiger (Eds.), What if there were no significance tests? (pp. 319-332). Mahwah, NJ: Erlbaum.
44. Roberts, S., & Pashler, H. (2000). How persuasive is a good fit? A comment on theory testing. Psychological Review, 107, 358-367.
45. Rosenthal, R., & Rosnow, R. L. (1991). Essentials of behavioral research: Methods and data analysis (2nd ed.). New York: McGraw-Hill.
46. Rozeboom, W. W. (1960). The fallacy of the null-hypothesis significance test. Psychological Bulletin, 57, 416-428.
47. Rozeboom, W. W. (1997). Good science is abductive, not hypothetico-deductive. In L. Harlow, S. A. Mulaik, & J. H. Steiger (Eds.), What if there were no significance tests? (pp. 335-391). Mahwah, NJ: Erlbaum.
48. Salmon, W. C. (1990). Rationality and objectivity in science or Tom Kuhn meets Tom Bayes. In C. W. Savage (Ed.), Minnesota studies in the philosophy of science (Vol. 14, pp. 175-204). Minneapolis: University of Minnesota Press.
49. Savage, L. J. (1954). The foundations of statistics. New York: Wiley.
50. Schmidt, F. L. (1996). Statistical significance testing and cumulative knowledge in psychology: Implications for the training of researchers. Psychological Methods, 1, 115-129.
51. Schmidt, F. L., & Hunter, J. E. (1997). Eight objections to the discontinuation of significance testing in the analysis of research data. In L. Harlow, S. A. Mulaik, & J. H. Steiger (Eds.), What if there were no significance tests? (pp. 37-64). Mahwah, NJ: Erlbaum.
52. Seidenfeld, T. (1979). Philosophical problems of statistical inference: Learning from R. A. Fisher. Dordrecht, Holland: D. Reidel.
53. Sellke, T., Bayarri, M. J., & Berger, J. O. (2001). Calibration of p values for testing precise null hypotheses. American Statistician, 55, 62-71.
54. Suppes, P. (1994). Qualitative theory of subjective probability. In G. Wright & P. Ayton (Eds.), Subjective probability (pp. 17-38). Chichester, England: Wiley.
55. Trafimow, D. (2003). Hypothesis testing and theory evaluation at the boundaries: Surprising insights from Bayes's theorem. Psychological Review, 110, 526-535.
56. Trafimow, D. (2005). The ubiquitous Laplacian assumption: Reply to Lee and Wagenmak-ers. Psychological Review, 112, 669-674.
57. Trafimow, D. (2007). Multiplicative invalidity and its application to complex correlational models. Genetic, Social, and General Psychology Monographs, 132, 215-239.