10 - Subjective priors  pp. 225-243

Subjective priors

By Michael A. McCarthy

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Because priors can have an important influence on the posterior distributions, their construction needs to be logical and repeatable. Subjectively generated priors, when combined with new data using Bayes' rule, indicate how a person's belief in parameter values should be updated to accommodate the new data. It is not surprising that such subjective treatments of knowledge raise concerns among scientists (e.g. Dennis, 1996). Is one person's subjective judgement a particularly valid basis for making scientific inferences?

Subjective judgement is useful for science in several circumstances. These include using subjective judgement to help interpret data, understanding how data can turn differences of opinion into agreement, and using subjective judgements coherently and explicitly in cases where time, resources and data are limited. Bayesian methods in these cases provide a more transparent treatment of that subjective judgement than either pretending it does not exist or considering the judgements qualitatively. An advantage of a Bayesian approach is that the subjective judgement can be combined logically with data. However, the use of subjective judgement is not inherently Bayesian; other approaches are available (Ayyub, 2001).

The process of eliciting subjective judgements should be documented and repeatable. Individual elicitation case studies differ in how questions are asked, how differences of opinion are handled, and how elicited information is used and combined with other sources of data (Ayuub, 2001; Burgman, 2005).


Reference Title: References

Reference Type: reference-list

Reference Type: reference-list

Adcock, C. J. (1997). Sample size determination: a review. The Statistician, 46, 261–83.
Agresti, A. (1990). Categorical Data Analysis. New York, USA: Wiley.
Akaike, H. (1973). Information theory as an extension of the maximum likelihood principle. Second International Symposium on Information Theory, ed. B. N. Petrov and F. Caski . Budapest: Akademiai Kiado, pp. 267–81.
Akçakaya, H. R. (1990). A method for simulating demographic stochasticity. Ecological Modelling, 54, 133–36.
Albert, J. H. (1997). Bayesian testing and estimation of association in a two-way contingency table. Journal of the American Statistical Association, 92, 685–93.
Alefeld, G. and Herzberger, J. (1983). Introduction to Interval Computations. New York, USA: Academic Press.
Anderson, D. R. , Burnham, K. P. and Thompson, W. L. (2000). Null hypothesis testing: problems, prevalence and an alternative. Journal of Wildlife Management, 64, 912–23.
Anderson, J. L., (1998). Embracing uncertainty: the interface of Bayesian statistics and cognitive psychology. Conservation Ecology 2, http://www.ecologyandsociety.org/vol2/iss1/art2/
Arnquist, G. and Wooster, D. (1995). Meta-analysis: synthesizing research findings in ecology and evolution. Trends in Ecology and Evolution, 10, 236–40.
Attiwill, P. M. and Leeper, G. W. (1987). Forest Soils and Nutrient Cycles. Carlton, Australia: Melbourne University Press.
Ayton, P. and Wright, G. (1994). Subjective probability: what should we believe? In Subjective Probability, ed. G. Wright and P. Ayton . New York, USA: Wiley, pp. 163–84.
Ayyub, B. M. (2001). Elicitation of Expert Opinions for Uncertainty and Risks. Boca Raton, USA: CRC Press.
Bakan, D. (1966). The test of significance in psychological research. Psychological Bulletin, 66, 423–37.
Balakrishnan, N. and Nevzorov, V. B. (2003). A Primer on Statistical Distributions. Hoboken, NJ, USA: Wiley.
Bayes, T. R. (1763). An essay towards solving a problem in the doctrine of chances. Philosophical Transactions, 53, 370–418.
Begon, M. , Townsend, C. and Harper, J. (2005). Ecology: From Individuals to Ecosystems, 4th edn. Malden, MA, USA: Blackwell.
Belia, S. , Fidler, F. , Williams, F. and Cumming G. (2005). Researchers misunderstand confidence intervals and standard error bars. Psychological Methods, 10, 389–96.
Bellhouse, D. R. (2004). The Reverend Thomas Bayes, FRS: A Biography to Celebrate the Tercentenary of His Birth. Statistical Science, 19, 3–43.
Ben-Haim, Y. (2001). Information-gap Decision Theory: Decisions Under Severe Uncertainty. San Diego, USA: Academic Press.
Berger, J. O. (1985). Statistical Decision Theory and Bayesian Analysis. New York, USA: Springer-Verlag.
Berger, J. O. and Sellke, T. (1987). Testing a point null hypothesis: the irreconcilability of P values and evidence. Journal of the American Statistical Association, 82, 112–22.
Berger, J. O. and Berry, D. A. (1988). Statistical analysis and the illusion of objectivity. American Scientist, 76, 159–65.
Bilodeau, M. and Brenner, D. (1999). Theory of Multivariate Statistics. New York: Springer-Verlag.
Bondi, H. (2004). Correspondence: Statistics don't support cot-death murder theory: Misunderstanding of statistics is widespread and has led to miscarriages of justice. Nature, 428, 799.
Bormann, F. H. and Likens, G. E. (1979). Pattern and Process in a Forested Ecosystem. New York, USA: Springer-Verlag.
Brack, C. L. (2002). Pollution mitigation and carbon sequestration by an urban forest. Environmental Pollution, 116, S195–S200.
Brereton, R. , Mallick, S. A. and Kennedy, S. J. (2004). Foraging preferences of swift parrots on Tasmanian blue-gum: tree size, flowering frequency and flowering intensity. Emu, 104, 377–83.
Brooks, S. P. and Gelman, A. (1998). General methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics, 7, 434–55.
Broome, L. S. and Geiser, F. (1995). Hibernation in free-living Mountain Pygmy-possums, Burramys parvus (Marsupialia: Burramyidae). Australian Journal of Zoology, 43, 373–79.
Brühl, C. A. , Mohamed, V. and Linsenmair, K. E. (1999). Altitudinal distribution of leaf litter ants along a transect in primary forests on Mount Kinabalu, Sabah, Malaysia. Journal of Tropical Ecology, 15, 265–77.
Burgman, M. A. , Ferson, S. and Akcakaya, H. R. (1993). Risk Assessment in Conservation Biology. London, UK: Chapman and Hall.
Burgman, M. (2005). Risks and Decisions for Conservation and Environmental Management. Cambridge, UK: Cambridge University Press.
Burley, N. , Krantzberg, G. and Radman, P. (1982). Influence of colour-banding on the conspecific preferences of zebra finches. Animal Behaviour, 30, 444–55.
Burnham, K. P. and Anderson, D. R. (2002). Model Selection and Multi-Model Inference: a Practical Information Theoretic Approach. New York, USA: Springer-Verlag.
Calder, W. A. (1984). Size, Function, and Life History. Cambridge, MA, USA: Harvard University Press.
Carlin, B. P. and Chib, S. (1995). Bayesian model choice via Markov chain Monte Carlo methods. Journal of the Royal Statistical Society, B57, 473–84.
Carver, R. P. (1978). The case against statistical testing. Harvard Educational Review, 48, 378–99.
Chambers, J. M. , Cleveland, W. S. , Kleiner, B. and Tukey, P. A. (1983). Graphical Methods for Data Analysis, CA, USA: Wadsworth.
Christensen, D. L. , Herwig, B. R. , Schindler, D. E. and Carpenter, S. R. (1996). Impacts of lakeshore residential development on coarse woody debris in north temperate lakes. Ecological Applications, 6, 1143–49.
Chib, S. and Greenberg, E. (1995). Understanding the Metropolis-Hastings algorithm. The American Statistican, 49, 327–35.
Clark, C. A. (1963). Hypothesis testing in relation to statistical methodology. Review of Educational Research, 33, 455–73.
Clark, J. S. (2005). Why environmental scientists are becoming Bayesians. Ecology Letters, 8, 2–15.
Clarke, R. D. (1972). The effect of toe clipping on survival in Fowler's toad (Bufo woodhousei fowleri). Copeia, 1972, 182–85.
Cohen, J. (1994). The earth is round (p<.05). American Psychologist, 49, 997–1003.
Congdon, P. (2003). Applied Bayesian Modelling. Chichester, UK: Wiley.
Cox, R. T. (1946). Probability, frequency and reasonable expectation. American Journal of Physics, 14, 1–13.
Crome, F. H. J. , Thomas, M. R. and Moore, L. A. (1996). A novel Bayesian approach to assessing impacts of rain forest logging. Ecological Applications, 6, 1104–23.
Dale, A. I. (1999) 2nd edn. A History of Inverse Probability from Thomas Bayes to Karl Pearson. New York: Springer.
Deming, W. E. (1975). On probability as a basis for action. American Statistician, 29, 146–52.
Dennis, B. (1996). Discussion: should ecologists become Bayesians? Ecological Applications, 6, 1095–1103.
Draper, D. (1995). Assessment and propagation of model uncertainty (with discussion). Journal of the Royal Statistical Society, B57, 45–97.
Dunning, J. B., Jr (1993). CRC Handbook of Avian Body Masses. Boca Raton, FL, USA: CRC Press.
Edwards, A. W. F. (1992). Likelihood: an Account of the Statistical Concept of Likelihood and its Application to Scientific Inference. Cambridge, UK: Cambridge University Press.
Elgar, M. A. , Allan, R. A. and Evans, T. A. (1996). Foraging strategies in orb-spinning spiders: ambient light and silk decorations in Agriope aetherea Walckenaer (Araneae: Araneoidea). Australian Journal of Ecology, 21, 464–67.
Elith, R. J. (2002). Predicting the distribution of plants. Ph.D. thesis, University of Melbourne, Parkville, Australia.
Ellison, A. M. (1996). An Introduction to Bayesian inference for ecological research and environment decision-making. Ecological Applications, 6, 1036–46.
Ellison, A. M. (2001). Exploratory data analysis and graphical display. In Design and Analysis of Ecological Experiments, 2nd edn, ed. S. M. Scheiner and J. Gurevitch . Oxford: Oxford University Press, pp. 37–62.
Ellison, A. M. (2004). Bayesian inference in ecology. Ecology Letters, 7, 509–20.
Ferson, S. (2005). Bayesian Methods in Risk Assessment. http://www.ramas.com/bayes.pdf
Ferson, S. (2002). RAMAS Risk Calc 4.0 Software: Risk Assessment with Uncertain Numbers. Boca Raton, USA: Lewis Publishers.
Fidler, F. (2005). From Statistical Significance to Effect Estimation: Statistical Reform in Psychology, Medicine and Ecology. Ph.D. thesis, University of Melbourne, Parkville, Australia.
Fidler, F. , Cumming, G. , Burgman, M. and Thomason, N. (2004). Statistical reform in medicine, psychology and ecology. The Journal of Socio-Economics, 33, 615–30.
Fidler, F., Burgman, M. A., Cumming, G., Buttrose, R. and Thomason, N. (2006). Impact of criticism of null hypothesis significance testing on statistical reporting practices in conservation biology. Conservation Biology, 20, 1539–44.
Fisher, R. F. (1930). Inverse probability. Proceedings of the Cambridge Philosophical Society, 26, 528–35.
Flueck, W. T. (2001). Offspring sex ratio of introduced red deer in Patagonia, Argentina after an intensive drought. Journal of Neotropical Mammalogy, 8, 139–47.
Forrester, G. E. and Steele, M. A. (2004). Predators, prey refuges, and the spatial scaling of density-dependent prey mortality. Ecology, 85, 1332–42.
Fowler, J. , Cohen, L. and Jarvis, P. (1998). Practical Statistics for Field Biology, 2nd edn. Chichester, UK: Wiley.
French, K. and Westoby, M. (1996) Vertebrate-dispersed species in a fire-prone environment. Australian Journal of Ecology, 21, 379–85.
Gauthier-Clerc, M. , Gendner, J.-P. , Ribic, C. A. , Fraser, W. R. , Woehler, E. J. , Descamps, S. , Gilly, C. , Le Bohec, C. and Le Maho, Y. (2004). Long-term effects of flipper bands on penguins. Proceedings of the Royal Society of London, B271, S423–26.
Gelman, A. and Meng, X.-L. (1996). Model checking and model improvement. In Markov Chain Monte Carlo in Practice, ed. W. R. Gilks , S. Richardson and D. J. Spiegelhalter . London, UK: Chapman and Hall, pp. 189–201.
Gelman. A. , Carlin, J. B. , Stern, H. S. and Rubin, D. B. (2004). Bayesian Data Analysis, 2nd edn. Boca Raton, FL, USA: Chapman and Hall/CRC.
Gibbons, P. and Lindenmayer, D. B. (2002). Tree Hollows and Wildlife Conservation in Australia. Melbourne, Australia: CSIRO Publishing.
Gigerenzer, G. and Hoffrage, U. (1995). How to improve Bayesian reasoning without instruction: frequency formats. Psychological Review, 102 (4), 684–704.
Gilks, W. R. , Richardson, S. and Spiegelhalter, D. J. (1996). Markov Chain Monte Carlo in Practice. London, UK: Chapman and Hall.
Gilpin, M. E. and Soulé, M. E. (1986). Minimum viable populations: processes of species extinctions. Conservation Biology: the Science of Scarcity and Diversity, ed. M. E. Soulé . Sunderland, MA, USA: Sinauer, pp. 19–34.
Ginzburg, L. R. , Slobodkin, L. B. , Johnson, K. and Bindman, A. G. (1982). Quasiextinction probabilities as a measure of impact on population growth. Risk Analysis, 2, 171–81.
Gotelli, N. J. and Arnett, A. E. (2000). Biogeographic effects of red fire ant invasion. Ecology Letters, 3, 257–61.
Gotelli, N. J. and Ellison, A. M. (2004). A Primer of Ecological Statistics. Sunderland, MA, USA: Sinauer.
Grand, J. B. , Flint, P. L. , Peterson, M. R. and Moran, C. L. (1998). Effect of lead poisoning on spectacled eider survival rates. Journal of Wildlife Management, 62, 1103–9.
Green, P. T. (1997). Red crabs in rain forest on Christmas Island, Indian Ocean: activity patterns, density and biomass. Journal of Tropical Ecology, 13, 17–38.
Gurevitch, J. and Hedges, L. V. (2001). Meta-analysis: combining the results of independent experiments. Design and Analysis of Ecological Experiments, eds. S. M. Scheiner and J. Gurevitch , 2nd edn. Oxford, UK: Oxford University Press, pp. 347–69.
Haller, H. and Krauss, S. (2002). Misinterpretations of significance: a problem students share with their teachers? Methods of Psychological Research Online, 7 (1), pp. 1–20.
Hampton, J. M. , Moore, P. G. and Thomas, H. (1973). Subjective probability and its measurement. Journal of the Royal Statistical Society, Series A, 136, 21–42.
Harper, M. J. , McCarthy, M. A. and van der Ree, R. (2005). The abundance of hollow-bearing trees in urban dry sclerophyll forest and the effect of wind on hollow development. Biological Conservation, 122, 181–92.
Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57, 97–109.
Hero, J.- M. (1989). A simple code for toe clipping anurans. Herpetological Review, 20, 66–7.
Hilborn, R. and Mangel, M. (1997). The Ecological Detective: Confronting Models with Data. Princeton, NJ, USA: Princeton University Press.
Hill, R. (2004). Multiple sudden infant deaths–coincidence or beyond coincidence? Paediatric and Perinatal Epidemiology, 18, 320–26.
Hoenig J. M. and Heisey D. M. (2001). The abuse of power: the pervasive fallacy of power calculations for data analysis. The American Statistician, 55, 19–24.
Hoeting, J. A. , Madigan, D. , Raftery, A. E. and Volinsky, C. T. (1999). Bayesian model averaging: a tutorial. Statistical Science, 14, 382–401.
Howson, C. and Urbach, P. (1991). Bayesian reasoning in science. Nature, 350, 371–4.
Huang, Y. J. (1987). The potential of vegetation in reducing summer cooling loads in residential buildings. Journal of Climate and Applied Meteorology, 26, 1103–16.
Humphries, R. B. (1979). Dynamics of a Breeding Frog Community. Ph.D. thesis. The Australian National University.
Hunt, S. , Cuthill, I. C. , Swaddle, J. P. and Bennett, A. T. D. (1997). Ultraviolet vision and band-colour preferences in female zebra finches, Taeniopygia guttata. Animal Behaviour, 54, 1383–92.
Jaynes, E. T. (1976). Confidence intervals vs. Bayesian intervals. Foundations of Probability Theory, Statistical Inference, and Statistical Theories of Science, II, eds. W. L. Harper and C. A. Hooker . Dordrecht, Holland: Reidel, pp. 175–213.
Jaynes, E. T. (2003). Probability Theory: The Logic of Science. New York, USA: Cambridge University Press.
Jeffreys, H. (1961). Theory of Probability, 3rd edn. Oxford, UK: Oxford University Press.
Johnson, N. L. , Kotz, S. and Balakrishnan, N. (1994). Continuous Univariate Distributions, 1, 2nd edn. New York, USA: Wiley.
Johnson, N. L. , Kotz, S. and Balakrishnan, N. (1995). Continuous Univariate Distributions, 2, 2nd edn. New York, USA: Wiley.
Johnson, N. L. , Kotz, S. and Balakrishnan, N. (1997). Discrete Multivariate Distributions, 2nd edn. New York, USA: Wiley.
Johnson, N. L. , Kotz, S. and Kemp, A. W. (1992). Univariate Discrete Distributions, 2nd edn. New York, USA: Wiley.
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, 763–72.
Johnston, J. P. , Peach, W. J. , Gregory, R. D. and White, S. A. (1997). Survival rates of tropical and temperate passerines: a Trinidadian perspective. American Naturalist, 150, 771–89.
Joyce, H. (2002). Beyond reasonable doubt. Plus Magazine, 21 (http://pass.maths.org.uk/issue21/features/clark/index.html)
Kahneman, D. , Slovic, P. and Tversky A. (eds.) (1982). Judgement Under Uncertainty: Heuristics and Biases. New York: Cambridge University Press.
Kass, R. E. and Raftery, A. E. (1995). Bayes factors and model uncertainty. Journal of the American Statistical Association, 90, 773–95.
Kaufmann, A. and Gupta, M. M. (1985). Introduction to Fuzzy Arithmetic. New York, USA: Reinhold.
Knuth, D. E. (1997). The Art of Computer Programming. Semi-numerical Algorithms, 2, 3rd edn. Reading, MA, USA: Addison-Wesley.
Körtner, G. and Geiser, F. (1998). Ecology of natural hibernation in the marsupial mountain pygmy-possum (Burramys parvus). Oecologia, 113, 170–78.
Kotz, S. , Balakrishnan, N. and Johnson, N. L. (2000). Continuous Multivariate Distributions, 2nd edn. New York, USA: Wiley.
Lebreton, J.-D. , Burnham, K. P. , Clobert, J. and Anderson, D. R. (1992). Modeling survival and testing biological hypotheses using marked animals: a unified approach with case studies. Ecological Monographs, 62, 67–118.
Lemckert, F. (1996). Effects of toe-clipping on the survival and behaviour of the Australian frog Crinia signifera . Amphibia-Reptilia, 17, 287–90.
Lindley, D. V. (1997). The choice of sample size. The Statistician, 46, 129–38.
Lindley, D. V. and Phillips, L. D. (1976). Inference for a Bernoulli process (a Bayesian view). The American Statistician, 30, 112–19.
Link, W. A. and Barker, R. J. (2006). Model weights and the foundation of multimodel inference. Ecology, 87, 2626–35.
Lüddecke, H. and Amézquita, A. (1999). Assessment of disc clipping on the survival and behaviour of the Andean frog Hyla labialis. Copeia, 1999, 824–30.
Ludwig, D. (1996). Uncertainty and the assessment of extinction probabilities. Ecological Applications, 6, 1067–76.
Mackenzie, D. I. , Nichols, J. D. , Lachman, G. B. , Droege, S. , Royle, J. A. and Langitimm, C. A. (2002). Estimating site occupancy rates when detection probabilities are less than one. Ecology, 83, 2248–55.
Mansergh, I. , Baxter, B. , Scotts, D. , Brady, T. and Jolley, D. (1990). Diet of Burramys parvus (Marsupialia: Burramyidae) and other small mammals in the alpine environment at Mt Higginbotham, Victoria. Australian Mammalogist, 13, 167–77.
Mansergh, I. M. and Broome, L. S. (1994). The Mountain Pygmy-possum of the Australian Alps. Sydney, Australia: University of New South Wales Press.
Martin, T. G. , Kuhnert, P. M. , Mengersen, K. and Possingham, H. P. (2005). The power of expert opinion in ecological models using Bayesian methods: impact of grazing on birds. Ecological Applications, 15, 266–80.
Marzolin, G. (1988). Polygynie du Cincle pongeur (Cinclus cinclus) dans les côtes de Lorraine. L'Oiseau et la Revue Francaise d'Ornithologie, 58, 277–86.
Masters, P. (1993). The effects of fire-driven succession and rainfall on small mammals in spinifex grasslands at Uluru National Park, Northern Territory. Wildlife Research, 20, 803–13.
Masters, P. , Dickman, C. and Crowther, M. (2003). The effects of cover reduction on Mulgara (Dasycercus cristicauda), rodent and invertebrate populations in central Australia: implications for management. Austral Ecology, 28, 658–65.
May, R. M. (2004). Ethics and amphibians. Nature, 431, 403.
McCarthy, M. A. (1996). Red kangaroo (Macropus rufus) dynamics: effects of rainfall, harvesting, density dependence and environmental stochasticity. Journal of Applied Ecology, 33, 45–53.
McCarthy, M. A. (1997). Competition and dispersal from multiple nests. Ecology, 78, 873–83.
McCarthy, M. A. and Parris, K. M. (2004). Clarifying the effect of toe clipping on frogs with Bayesian statistics. Journal of Applied Ecology, 41, 780–86.
McCarthy, M. A. and Broome, L. S. (2000). A method for validating stochastic models of population viability: a case study of the mountain pygmy-possum (Burramys parvus). Journal of Animal Ecology, 69, 599–607.
McCarthy, M. A. and Thompson, C. (2001). Expected minimum population size as a measure of threat. Animal Conservation, 4, 351–55.
McCarthy, M. A. and Masters, P. (2005). Profiting from prior information in Bayesian analyses of ecological data. Journal of Applied Ecology, 42, 1012–19.
McCarthy, M. A. , Franklin, D. C. and Burgman, M. A. (1994). The importance of demographic uncertainty: an example from the helmeted honeyeater. Biological Conservation, 67, 135–42.
McCarthy, M. A. , Webster, A. , Loyn, R. H. and Lowe, K. W. (1999). Uncertainty in assessing the viability of the powerful owl Ninox strenua in Victoria, Australia. Pacific Conservation Biology, 5, 144–54.
McCullagh, P. and Nelder, J. A. (1989). Generalized Linear Models, 2nd edn, London, UK: Chapman and Hall.
McLean, N. (2003). Ecology and Management of Overabundant Koala (Phascolarctos cinereus) Populations. Ph.D. thesis. University of Melbourne, Parkville, Australia.
McPherson, E. G. , Scott, K. I. and Simpson, J. R. (1998). Estimating cost effectiveness of residential yard trees for improving air quality in Sacramento, California, using existing models. Atmospheric Environment, 32, 75–84.
Metropolis H. , Rosenbluth A. W. , Rosenbluth M. N. , Teller A. H. and Teller E. (1953). Equations of state calculations by fast computing machines. Journal of Chemical Physics, 21, 1087.
Morgan, M. G. and Henrion, M. (1990). Uncertainty: a Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis. Cambridge, UK: Cambridge University Press.
Mullan Crain, C. , Silliman, B. R. , Bertness, S. L. and Bertness, M. D. (2004). Physical and biotic drivers of plant distribution across estuarine salinity gradients. Ecology, 85, 2539–49.
O'Donnell, T. (1936). History of Life Insurance in Its Formative Years; Compiled from Approved Sources. Chicago, USA: American Conservation Company.
O'Hagan, A. and Luce, B. R. (2003). A primer on Bayesian statistics in health economics and outcomes research. Bayesian Initiative in Health Economics & Outcomes Research. Bethesda, Maryland: Bayesian Initiative in Health Economics and Outcomes Research; Sheffield, UK: The Centre for Bayesian Statistics in Health Economics.
Oakes, M. (1986). Statistical Inference: A Commentary for the Social and Behavioural Sciences. Chichester, UK: John Wiley & Sons Ltd.
Parkhurst, D. F. (1997). Commentaries on Significance Testing. http://www.indiana.edu/∼stigtsts/index.html#contents
Parris, K. M. (2001). Distribution, habitat requirements and conservation of the cascade treefrog (Litoria pearsoniana, Anura: Hylidae). Biological Conservation, 99, 285–92.
Parris, K. M. and McCarthy, M. A. (2001). Identifying effects of toe-clipping on anuran return rates: the importance of statistical power. Amphibia-Reptilia, 22, 275–89.
Parris, K. M. (2006). Urban amphibian assemblages as meacommunities. Journal of Animal Ecology, 75, 757–64.
Paruelo, J. M. and Laueroth, W. K. (1996). Relative abundance of plant functional types in grasslands and shrublands of North America. Ecological Applications, 6, 1212–24.
Peterman, R. M. (1990). Statistical power analysis can improve fisheries research and management. Canadian Journal of Aquatic Sciences, 47, 2–15.
Peters, R. H. (1983). The Ecological Implications of Body Size. Cambridge, UK: Cambridge University Press.
Polis, G. A. , Hurd, S. D. , Jackson, C. D. and Sanchez-Piñero, F. (1998). Multifactor population limitation: variable spatial and temporal control of spiders on Gulf of California islands. Ecology, 79, 490–502.
Press, W. H. , Teukolsky, S. A. , Vetterling, W. T. and Flannery, B. P. (1992). Numerical Recipes in C: The Art of Scientific Computing. Cambridge, UK: Cambridge University Press.
Quinn, G. P. and Keough, M. J. (2002). Experimental Design and Data Analysis. Cambridge, UK: Cambridge University Press.
Richards, S. A. (2005). Testing ecological theory using the information-theoretic approach: examples and cautionary results. Ecology, 86, 2805–14.
Rozeboom, W. W. (1997). Good science is abductive, not hypothetico-deductive. In What If There Were No Significance Tests?, ed. L. L. Harlow , S. A. Mulaik and J. H. Steiger . Hillsdale, NJ, USA: Erlbaum, pp. 335–92.
Savage, V. M. , Gillooly, J. F. , Brown, J. H. , West, G. B. and Charnov, E. L. (2004). Effects of body size and temperature on population growth. American Naturalist, 163, 429–41.
Shaffer, M. L. (1981). Minimum population sizes for species conservation. Bioscience, 31, 131–4.
Smith, A. and Broome, L. S. (1992). The effects of environment and sex on the diet of the Mountain Pygmy-possum and its implications for the species' conservation and management in south-east Australia. Australian Wildlife Research, 19, 755–68.
Sokal, R. R. and Rohlf, F. J. (1995). Biometry: The Principles and Practice of Statistics in Biological Research, 3rd edn. New York: W. H. Freeman and Co.
Spiegelhalter, D. , Thomas, A. , Best, N. and Lunn, D. (2005). WinBUGS User Manual Version 2.10. Cambridge, UK: MRC Biostatistics Unit.
Spiegelhalter, D. J , Best, N. G. , Carlin, B. P. and van der Linde, A. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B, 64, 583–639.
Stephens, P. A. , Buskirk, S. W. , Hayward, G. D. and Martínez Del Rio, C. (2005). Information theory and hypothesis testing: a call for pluralism. Journal of Applied Ecology, 42, 4–12.
Stow, C. A. and Borsuk, M. E. (2003). Enhancing causal assessment of estuarine fishkills using graphical models. Ecosystems, 6, 11–19.
Taylor, B. L. and Gerrodette, T. (1993). The uses of statistical power in conservation biology: The vaquita and Northern Spotted Owl. Conservation Biology, 7, 489–500.
Trivers, R. L. and Willard, D. E. (1973). Natural selection of parental ability to vary the sex ratio of offspring. Science, 179, 90–1.
Tukey, J. W. (1997). Exploratory data analysis. Reading, MA, USA: Addison-Wesley.
Tversky, A. and Kahneman, D. (1974). Judgment under uncertainty: heuristics and biases. Science, 185, 1124–31.
Tyre, A. J. , Tenhumberg, B. , Field, S. , Possingham, H. P. , Niejalke, D. and Parris, K. (2003). Improving precision and reducing bias in biological surveys by estimating false negative error rates in presence-absence data. Ecological Applications, 13, 1790–1801.
Underwood, A. J. (1997). Experiments in Ecology: Their Logical Design and Interpretation Using Analysis of Variance. Cambridge, UK: Cambridge University Press.
Volinsky, C. T. , Madigan, D. , Raftery, A. E. and Kronmal, R. A. (1997). Bayesian model averaging in proportional hazard models: predicting the risk of a stroke. Applied Statistics, 46, 433–48.
Wade, P. R. (2000). Bayesian methods in conservation biology. Conservation Biology, 14, 1308–16.
Waichman, A. V. (1992). An alphanumeric code for toe clipping amphibians and reptiles. Herpetological Review, 23, 19–21.
Walley, P. (1991). Statistical Reasoning with Imprecise Probabilities. London, UK: Chapman and Hall.
West, B. G. , James, H. , Brown, J. H. and Enquist, B. J. (1997). A general model for the origin of allometric scaling laws in biology. Science, 276, 122–6.
Williamson, I. and Bull, C. M. (1996). Population ecology of the Australian frog, Crinia signifera: adults and juveniles. Wildlife Research, 23, 249–66.
Wintle, B. A., and Bardos, D. C., (in press). Modelling species habitat relationships with spatially autocorrelated observation data. Ecological Applications.
Wintle, B. A. , McCarthy, M. A. , Parris, K. M. and Burgman, M. A. (2004). Precision and bias of methods for estimating point survey detection probabilities. Ecological Applications, 14, 703–12.
Wintle, B. A. , Kavanagh, R. P. , McCarthy, M. A. and Burgman, M. A. (2005a). Estimating and dealing with detectability in occupancy surveys for forest owls and arboreal marsupials. Journal of Wildlife Management, 69, 905–17.
Wintle, B. A. , Elith J. and Potts J. M. (2005b). Fauna habitat modelling and mapping: a review and case study in the Lower Hunter Central Coast region of NSW. Austral Ecology, 30, 719–38.
Wintle, B. A. , McCarthy, M. A. , Volinsky, C. T. and Kavanagh, R. P. (2003). The use of Bayesian Model Averaging to better represent uncertainty in ecological models. Conservation Biology, 17, 1579–90.
Zar, J. H. (1999). Biostatistical analysis. Upper Saddle River, NJ, USA: Prentice Hall.
Ziliak, S. and McCloskey, D. (2004). Size matters: the standard error of regressions in the American Economic Review. Journal of Socio-economics, 33, 527–47.