Stat 401A - References to additional material

This page contains references that may interest you if you wish to explore an issue further. None of these is required.

Use of randomization tests in biology / agriculture (general articles/books):
- Ludbrook and Dudley, 1998. Why permutation tests are superior to t and F tests in biomedical research. American Statistician 52:127-132.
  Surveys biomedical literature. Experimental groups usually constructed by random assignment of treatments to non random samples. This matches the randomization approach, not the more typical 'sample from a normal population' justification of the t or F tests.
- Manly, B.F.G. 1997. Randomization, Bootstrap and Monte Carlo Methods in Biology. 2nd ed. Chapman and Hall.
  Book length treatment of randomization tests and related techniques, with biological examples from many fields.
- Good, P. 1994. Permutation tests: a practical guide to resampling methods for testing hypotheses. Springer
  Includes theory of why randomization / permutation tests work. Chapters 11 on Which test should you use? and 12 on Publishing your results are readable and useful.
Examples of randomization tests in biology (some recent papers from diverse fields):
- Wagner, M et al. 2004. Computational protein biomarker prediction: a case study for prostate cancer. BMC BIOINFORMATICS 5: article 26
- Bull, LS, Haywood, J and Pledger, S. 2004 Components of phenotypic variation in the morphometrics of shearwater (Puffinus) species IBIS 146:38-45
- Crist, TO, et al. 2003. Partitioning species diversity across landscapes and regions: A hierarchical analysis of alpha, beta, and gamma diversity AMERICAN NATURALIST. 162:734-743
- Van de Vliet, P. et al. 2003. Assessing the additional impact of fitness training in depressed psychiatric patients receiving multifaceted treatment: a replicated single-subject design DISABILITY AND REHABILITATION 25:1344-1353
- Williamson, GR. 2003. Misrepresenting random sampling? A systematic review of research papers in the Journal of Advanced Nursing JOURNAL OF ADVANCED NURSING 44:278-288.
Choice of parametric or non-parametric methods:
- Seaman, J. W. Jr. and R. G. Jaeger. 1990. Statisticae dogmaticae: a critical essay on statistical practice in ecology. Herpetologica 46:337-346.
  Example of the strident argument for nonparametric methods. Followed by three discussions (pp 346-361).
- Stewart-Oaten, Allan, 1995. Rules and Judgments in Statistics: Three Examples. Ecology 76:2001-2009.
  In my opinion, a good non-dogmatic, accurate comparison of nonparametric and nonparametric tests.
- Montgomery, R. H. and Loftis, J. C. 1987. Applicability of the t-test for detecting trends in water quality variables. Water Resources Bulletin 23:653-662.
  Argues that t-test usually applicable because robust to the characteristics of water quality data.
- Helsel, D.R. and Hirsch, R. M. 1988. Discussion: Applicability of the t-test for detecting trends in water quality variables. Water Resources Bulletin 24:201-204.
  Argues that Montgomery and Loftis are wrong.
- Motulsky, H. 1995. Intuitive Biostatistics. Wiley, New York. Chapter 37 (Choosing a test), pp 297-306, discusses one approach to choosing a test. His words are different from mine, but I believe the concepts are the same.
- Weber, C.I. et al. 1989. Short-term methods for estimating the chronic toxicity of effluents and receiving waters to freshwater organisms. EPA/600/4-89/001. EPA Environmental Monitoring Systems Laboratory, Cincinnati.
Multiple comparisons
- Example where adjusting or not adjusting leads to very different policy, Women's Health Initiative study of long duration ostrogen: Discussed in Science (26 July 2002) 297:492. Two similar studies of risk and benefits of long term estrogen replacement therapy. One study is terminated early because one comparison indicates increased risk to participants. Simultaneous confidence intervals indicate no evidence of increased risk. Other study continued.
- Two papers from agricultural literature illustrating the diversity of opinions:
  Carmer, S.G. and Walker, W.M. 1982. Baby bear's dilemma: a statistical tale. Agronomy Journal 74:122-124.
  Argues for never using m.c.p. (i.e. always using l.s.d.) because it provides highest power.
  Peterson, R.G. 1977. Use and misuse of multiple comparison procedures. Agronomy Journal 69:205-208.
  Recommends m.c.p. when 'data snooping', i.e. when looking for interesting patterns. m.c.p. are 'almost never appropriate' for quantitative treatments, factorial treatment structures, and answering specific questions.
- Hsu, J.C. 1996. Multiple comparisons: theory and methods. Chapman and Hall/CRC. A good intermediate level text on many methods. Chapter 6 (pp 175-180) is full of good, useful advice.
False Discovery Rate
- Original paper: Benjamini and Hochberg, 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Royal Statistical Soc. B 57:289-300.
- Three comments/applications:
  Garci, L.V. 2003. Controlling the false discovery rate in ecological research. TREE 18:553-554.
  Fernando, R., Nettleton, D. et al. 2004. Controlling the proportion of false positives in multiple dependent tests. Genetics 166:611-619. By the "home team".
  Genovese, C.R. et al. 2002. Thresholding of statistical maps in functional neuroimaging using the false discovery rate. Neuroimage 15:870-878.

Residual analysis and model diagnostics:

Practical advice:

Riley, J. 2001. Presentation of statistical analysis. Experimental Agriculture. 37:115-123.
Morse, P.M. and Thompson, B.K. 1981. Presentation of experimental results. Canadian Journal of Plant Science 61:799-802