**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

Date |
Person |
Topic |
Reading |

8/28 | Eric | Informational Meeting | No paper this week |

9/4 | Ben, Lexi | Confirmatory Analysis Bias | Anderson et al. 2000 |

9/11 | Amanda | Bonferroni | Moran 2003 |

9/18 | Marla | Stepwise Regression | Whittingham et al 2006, Supplement |

9/25 | Becky | Transformations | Warton & Hui 2011 |

10/2 | Pamela | AIC missteps | Burnham et al. 2011 |

10/9 | Jason | Error Bars | Belia et al. 2005 |

10/16 | Natasha | Using Excel | McCullough & Heiser 2008 |

10/23 | Robyn | Pseudoreplication etc | Hurlbert 1984 |

10/30 | Erin | Residuals | Garcia-Berthou 2001 |

11/6 | Kristy | Mixed Models | Bennington & Thayne 1994 |

11/13 | Brian | One-tailed Tests | Lombardi & Hurlbert 2009 |

11/20 | Daniel | Positive Results Bias | Martínez-Abraín 2013 |

11/27 | No Class - Thanksgiving | ||

12/4 | Gaya | Bayesian Approaches | Dennis 2004 |

**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**