BIOL 708/808 Ecological Sciences Seminar: Statistical Misuse In Ecology

Wednesday 5:15-6:15 pm, BAL 2067

Format of the class

The class will meet each week. Each student will sign up for a topic. During that student's week, they will present the assigned paper on their topic. The leader for that week will lead a discussion of the assigned paper.

Grading:

50% journal article discussion

50% participation

Class Syllabus

Supplemental Readings

1. Confirmatory Analysis Bias

Anderson, D. R., K. P. Burnham, and W. L. Thompson. 2000. Null hypothesis testing: Problems, prevalence, and an alternative. Journal of Wildlife Management 64:912-923.
Anderson, D. R., W. A. Link, D. H. Johnson, and K. P. Burnham. 2001. Suggestions for presenting the results of data analyses. Journal of Wildlife Management 65:373-378.
Bakan, D. 1966. The test of significance in psychological research. Psychological Bulletin 66:423-437.
Baril, G. L. and J. T. Cannon. 1995. What is the probability that null hypothesis-testing is meaningless. American Psychologist 50:1098-1099.
Carver, R. P. 1978. The case against statistical significance testing. Harvard Educational Review 48:378-399.
Cohen, J. 1994. The earth is round (p<.05). American Psychologist 49:997-1003.
Cohen, J. 1995. The earth is round (p<.05): Rejoinder. American Psychologist 50:1103.
Frick, R. W. 1995. Accepting the null hypothesis. Memory and Cognition 23:132-138.
Frick, R. W. 1995. A problem with confidence-intervals. American Psychologist 50:1102-1103.
Gibbons, J. M., N. M. J. Crout, and J. R. Healey. 2007. What role should null-hypothesis significance tests have in statistical education and hypothesis falsification? Trends in Ecology & Evolution 22:445-446.
Gonzalez, R. 1994. The statistics ritual in psychological research. Psychological Science 5:321-328.
Hubbard, R. 1995. The earth is highly significantly round (p<.0001). American Psychologist 50:1098.
Johnson, D. H. 1995. Statistical sirens: The allure of nonparametrics. Ecology 76:1998-2000.
Johnson, D. H. 1999. The insignificance of statistical significance testing. Journal of Wildlife Management 63:763-772.
Johnson, D. H. 2002. The role of hypothesis testing in wildlife science. Journal of Wildlife Management 66:272-276.
Jones, D. and N. Matloff. 1986. Statistical hypothesis testing in biology: A contradiction in terms. Journal of Economic Entomology 79:1156-1160.
Loftus, G. R. 1993. A picture is worth a thousand p values: On the irrelevance of hypothesis testing in the microcomputer age. Behavior Research Methods, Instruments, & Computers 25:250-256.
Loftus, G. R. 1995. Data analysis as insight: Reply to morrison and weaver. Behavior Research Methods, Instruments, & Computers 27:57-59.
Loftus, G. R. and M. E. Masson. 1994. Using confidence intervals in within-subject designs. Psychonomic Bulletin & Review 1:476-490.
Matter, W. J. and R. W. Mannan. 1989. More on gaining reliable knowledge: A comment. Journal of Wildlife Management 53:1172-1176.
McGraw, K. O. 1995. Determining false alarm rates in null hypothesis-testing research. American Psychologist 50:1099-1100.
Morrison, G. R. and B. Weaver. 1995. Exactly how many p values is a picture worth? A commentary on loftus's plot-plus-error-bar approach. Behavior Research Methods, Instruments, & Computers 27:52-56.
Parker, S. 1995. The difference of means may not be the effect size. American Psychologist 50:1101-1102.
Richardson, J. T. E. 1996. Measures of effect size. Behavior Research Methods, Instruments, & Computers 28:12-22.
Rio, C. M. d., S. W. Buskirk, and P. A. Stephens. 2007. Response to gibbons et al.: Null-hypothesis significance tests in education and inference. Trends in Ecology & Evolution 22:446-446.
Romesburg, H. C. 1989. More on gaining reliable knowledge: A reply. Journal of Wildlife Management 53:1177-1180.
Romesburg, H. C. 1991. On improving the natural resources and environmental sciences. Journal of Wildlife Management 55:744-756.
Romesburg, H. C. 1993. On improving the natural resources and environmental sciences: A reply. Journal of Wildlife Management 57:184-189.
Shaver, J. P. 1993. What statistical testing is, and what it is not. Journal of Experimental Education 61:293-316.
Simberloff, D. 1990. Hypotheses, errors, and statistical assumptions. Herpetologica 46:351-357.
Stephens, P. A., S. W. Buskirk, and C. M. del Rio. 2007. Inference in ecology and evolution. Trends in Ecology & Evolution 22:192-197.
Stephens, P. A., S. W. Buskirk, G. D. Hayward, and C. Martinez del Rio. 2007. A call for statistical pluralism answered. Journal of Applied Ecology 44:461-463.
Stephens, P. A., S. W. Buskirk, G. D. Hayward, and C. MartÍnez Del Rio. 2005. Information theory and hypothesis testing: A call for pluralism. Journal of Applied Ecology 42:4-12.
Svyantek, D. J. and S. E. Ekeberg. 1995. The earth is round (so we can probably get there from here). American Psychologist 50:1101.
Townsend, J. T. 1994. Methodology and statistics in the behavioral sciences the old and the new. Psychological Science 5:321-325.
Wilkinson, L. and A. P. A. S. D. W. D. C. U. S. Task Force on Statistical Inference. 1999. Statistical methods in psychology journals: Guidelines and explanations. American Psychologist 54:594-604.

2. Bonferroni Correction

Moran, M. D. 2003. Arguments for rejecting the sequential bonferroni in ecological studies. Oikos 100:403-405.
Nakagawa, S. 2004. A farewell to bonferroni: The problems of low statistical power and publication bias. Behavioral Ecology 15:1044-1045.

3. Stepwise Regression

Flom, PL; Cassell, DL: Stopping stepwise: Why stepwise variable selection methods are bad, and what you should use. Given at SAS Global Forum, March, 2008

Hegyi, G. and L. Garamszegi. 2011. Using information theory as a substitute for stepwise regression in ecology and behavior. Behavioral Ecology and Sociobiology 65:69-76.

Whittingham, M. J., P. A. Stephens, R. B. Bradbury, and R. P. Freckleton. 2006. Why do we still use stepwise modelling in ecology and behaviour? Journal of Animal Ecology 75:1182-1189.

4. Transformations

O’Hara, R. B. and D. J. Kotze. 2010. Do not log-transform count data. Methods in Ecology and Evolution 1:118-122.

5. AIC missteps

Arnold, T. W. 2010. Uninformative parameters and model selection using akaike's information criterion. Journal of Wildlife Management 74:1175-1178.

Buckland, S. T., K. P. Burnham, and N. H. Augustin. 1997. Model selection: An integral part of inference. Biometrics 53:603-618.
Burnham, K. P., D. R. Anderson, and K. P. Huyvaert. 2011. Aic model selection and multimodel inference in behavioral ecology: Some background, observations, and comparisons. Behavioral Ecology and Sociobiology 65:23-35.
Cavanaugh, J. E. and R. H. Shumway. 1998. An akaike information criterion for model selection in the presence of incomplete data. Journal of Statistical Planning and Inference 67:45-65.
Dochtermann, N. and S. Jenkins. 2011. Developing multiple hypotheses in behavioral ecology. Behavioral Ecology and Sociobiology 65:37-45.
Forstmeier, W. and H. Schielzeth. 2011. Cryptic multiple hypotheses testing in linear models: Overestimated effect sizes and the winner's curse. Behavioral Ecology and Sociobiology 65:47-55.
Freckleton, R. P. 2011. Dealing with collinearity in behavioural and ecological data: Model averaging and the problems of measurement error. Behavioral Ecology and Sociobiology 65:91-101.
Garamszegi, L. 2011. Information-theoretic approaches to statistical analysis in behavioural ecology: An introduction. Behavioral Ecology and Sociobiology 65:1-11.
Hegyi, G. and L. Garamszegi. 2011. Using information theory as a substitute for stepwise regression in ecology and behavior. Behavioral Ecology and Sociobiology 65:69-76.
Mundry, R. 2011. Issues in information theory-based statistical inference—a commentary from a frequentist’s perspective. Behavioral Ecology and Sociobiology 65:57-68.
Nakagawa, S. and R. Freckleton. 2011. Model averaging, missing data and multiple imputation: A case study for behavioural ecology. Behavioral Ecology and Sociobiology 65:103-116.
Richards, S., M. Whittingham, and P. Stephens. 2011. Model selection and model averaging in behavioural ecology: The utility of the it-aic framework. Behavioral Ecology and Sociobiology 65:77-89.
Symonds, M. and A. Moussalli. 2011. A brief guide to model selection, multimodel inference and model averaging in behavioural ecology using akaike’s information criterion. Behavioral Ecology and Sociobiology 65:13-21.

6. Error Bars

Belia, S., F. Fidler, J. Williams, and G. Cumming. 2005. Researchers misunderstand confidence intervals and standard error bars. Psychological Methods 10:389-396.

Lanzante, J. R. 2005. A cautionary note on the use of error bars. Journal of Climate 18:3699-3703.

7. Using Excel

McCullough, B. D. and D. A. Heiser. 2008. On the accuracy of statistical procedures in microsoft excel 2007. Computational Statistics & Data Analysis 52:4570-4578.

Yalta, A. T. 2008. The accuracy of statistical distributions in microsoft® excel 2007. Computational Statistics & Data Analysis 52:4579-4586.

8. Pseudoreplication etc

Heffner, R. A., M. J. Butler, and C. K. Reilly. 1996. Pseudoreplication revisited. Ecology 77:2558-2562.

Hurlbert, S. H. 1984. Pseudoreplication and the design of ecological field experiments. Ecological Monographs 54:187-211.

Oksanen, L. 2001. Logic of experiments in ecology: Is pseudoreplication a pseudoissue? Oikos 94:27-38.

9. Residuals

GARCÍA-BERTHOU, E. 2001. On the misuse of residuals in ecology: testing regression residuals vs. the analysis of covariance. Journal of Animal Ecology 70:708-711.

Freckleton, R.P..2002 On the misuse of residuals in ecology: regression of residuals vs. multiple regression Journal of Animal Ecology 71:542-545.

10. Mixed Models

11. One-tailed Tests

12. Positive Results Bias

13. Bayesian Approaches