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References

  • Abelson, R.P. (1995). Statistics as Principled Argument. Hillsdale , NJ : Lawrence Erlbaum.
  • Altman, D.G. (1991). Practical Statistics for Medical Research. New York : Chapman and Hall.
  • Bart, J., Fligner, M.A. & Notz, W.I. (1998). Sampling and Statistical Methods for Behavioral Ecologists. Cambridge : Cambridge University Press.
  • Bender, R. & Lange, S. (2001). Adjusting for multiple testing – when and how? J. Clin. Epidemiol. 54, 343349.
  • Benjamini, Y. & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Roy. Stat. Soc. Ser. B Stat. Methodol. 57, 289300.
  • Benjamini, Y. & Yekutieli, D. (2001). The control of the false discovery rate in multiple testing under dependency. Annals Statist. 29, 11651188.
  • Benjamini, Y. & Yekutieli, D. (2005). Quantitative trait loci analysis using the false discovery rate. Genetics 171, 783790.
  • Benjamini, Y., Drai, D., Elmer, G., Kafkafi, N. & Golani, I. (2001). Controlling the false discovery rate in behavior genetics research. Behav. Brain Res. 125, 279284.
  • Benjamini, Y., Krieger, A.M. & Yekutieli, D. (2006). Adaptive linear step-up procedures that control the false discovery rate. Biometrika 93, 491507.
  • Box, G.E.P., Hunter, W.G. & Hunter, J.S. (2005). Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building, 2nd edn. New York : Wiley.
  • Braun, H.I., ed. (1994). The Collected Works of John W. Tukey, Volume VIII, Multiple Comparisons: 1948–1983. New York : Chapman and Hall.
  • Braver, S.L. (1975). On splitting the tails unequally: a new perspective on one- versus two-tailed tests. Educ. Psychol. Meas. 35, 283301.
  • Carmer, S.G. & Walker, W.M. (1982). Baby Bear's dilemma: A statistical tale. Agron. J. 74, 122124.
  • Christensen, R. (2005). Testing Fisher, Neyman, Pearson, and Bayes. Amer. Statist. 59, 121126.
  • Cowles, M. (1989). Statistics in Psychology: An Historical Perspective. Hillsdale, NJ : Lawrence Erlbaum.
  • Cox, D.R. (1958). Some problems connected with statistical inference. Ann. Math. Statist. 29, 357372.
  • Cox, D.R. (1965). A remark on multiple comparison methods. Technometrics 7, 223224.
  • Cox, D.R. (2006). Principles of Statistical Inference. Cambridge : Cambridge University Press.
  • Crabbe, J.C., Wahlsten, D. & Dudek, B.C. (1999). Genetics of mouse behavior: interactions with laboratory environment. Science 284, 16701672.
  • Cui, X., Hwang, J.T., Qiu, J., Blades, N.J. & Churchill, G.A. (2005). Improved statistical tests for differential gene expression by shrinking variance components estimates. Biostatistics 8, 414432.
  • Curran-Everett, D. (2000). Multiple comparisons: Philosophies and illustrations. Amer. J. Physiol.: Regul., Integr. Comp. Physiol. 279, R1R8.
  • Darlington, R.B. & Carlson, P.M. (1987). Behavioral Statistics. New York : The Free Press.
  • Day, R.W. & Quinn, G.P. (1989). Comparison of treatments after an analysis of variance in ecology. Ecol. Monogr. 59, 433463.
  • Dmitrienko, A. & Tamhane, A.C. (2007). Gatekeeping procedures with clinical trial applications. Pharm. Stat. 6, 171180.
  • Dmitrienko, A., Offen, W.W. & Westfall, P.H. (2003). Gatekeeping strategies for clinical trials that do not require all primary effects to be significant. Stat. Med. 22, 23872400.
  • Dmitrienko, A., Molenberghs, G., Chuang-Stein, C. & Offen, W. (2005). Analysis of Clinical Trials Using SAS: A Practical Guide. Cary , NC : SAS Institute.
  • Dmitrienko, A., Wiens, B.L., Tamhane, A.C. & Wang, X. (2007). Tree-structured gatekeeping tests in clinical trials with hierarchically ordered objectives. Stat. Med. 26, 24652478.
  • Dudoit, D., Shaffer, J.P. & Boldrick, J.C. (2003). Multiple hypothesis testing in microarray experiments. Statist. Sci. 18, 71103.
  • Duncan, D.B. (1965). A Bayesian approach to multiple comparisons. Technometrics 7, 171222.
  • Ecklund, G. & Seeger, P. (1965). Massignifikansanalys. Statistisk Tidskrift Stockholm, 3d series 4, 355365.
  • Eysenck, H.J. (1960). The concept of statistical significance and the controversy about one-tailed tests. Psych. Rev. 67, 269271.
  • Farcomeni, A. (2008). A review of modern multiple hypothesis testing, with particular attention to the false discovery proportion. Stat. Methods Med. Res. 17, 347388.
  • Fernando, R.L., Nettleton, D., Southey, R.R., Dekkers, J.C.M., Rothschild, M.F. & Soller, M. (2004). Controlling the proportion of false positives in multiple dependent tests. Genetics 166, 611619.
  • Finney, D.J. (1988). Was this in your statistics textbook? III. Design and analysis. Exp. Agric. 24, 421432.
  • Fleiss, J.L. (1981). Statistical Methods for Rates and Proportions, 2nd edn. New York : Wiley.
  • Fleiss, J.L. (1986). The Design and Analysis of Clinical Experiments. New York : Wiley.
  • Freedman, D., Pisani, R., Purves, R. & Adhibari, A. (1991). Statistics, 2nd edn. New York : Norton.
  • Gaines, S.D. & Rice, W.R. (1990). Analysis of biological data when there are ordered expectations. Amer. Nat. 135, 310317.
  • Garcia, L.V. (2004). Escaping the Bonferroni iron claw in ecological studies. Oikos 105, 657663.
  • Genovese, C.R., Lazar, N.A. & Nichols, T. (2002). Threshholding of statistical maps in functional neuroimaging using the false discovery rate. Neuroimage 15, 870878.
  • Genovese, C.R., Roeder, K. & Wasserman, L. (2006). False discovery control with p-value weighting. Biometrika 93, 509524.
  • Glass G.V. & Hopkins, K.D. (1984). Statistical Methods in Education and Psychology, 2nd edn. Englewood Cliffs , NJ : Prentice-Hall.
  • Goldfried, M.R. (1959). One-tailed tests and ‘unexpected’ results. Psych. Rev. 66, 7980.
  • Harnett, D.L. & Soni, A.K. (1991). Statistical Methods for Business and Economics, 4th edn. New York : Addison-Wesley.
  • Harris R.J. (2005a). Classical statistical inference extended: split-tailed tests. Encyclopedia of Statistics in Behavioral Science, vol. 1, eds. B.S. Everitt and D.C. Howell. pp. 263268. Chichester : Wiley.
  • Harris R.J. (2005b). Classical statistical inference: practice versus presentation. Encyclopedia of Statistics in Behavioral Science, vol. 1, eds. B.S. Everitt and D.C. Howell. pp. 268278. Chichester : Wiley.
  • Harris, R.J. & Quade, D. (1992). The minimally important difference significant criterion for sample size. J. Educ. Behav. Stat. 17, 2749.
  • Hawkins, D. (2005). Biomeasurement. New York : Oxford University Press.
  • Hochberg, Y. & Tamhane, A.C. (1987). Multiple Comparison Procedures. New York : Wiley.
  • Hoover, D.K. & Siegler, M.V. (2008). Sound and fury: McCloskey and significance testing in economics. J. Econ. Methodol. 15, 137.
  • Hung, H.M.J. & Wang, S.-J. (2009). Some controversial multiple testing problems in regulatory applications. J. Biopharm. Statist. 19, 111.
  • Hurlbert, S.H. (1990). Pastor binocularis: now we have no excuse [Review of The Design of Experiments, by R. Mead]. Ecology 71, 12221223.
  • Hurlbert, S.H. & Lombardi, C.M. (2003). Design and analysis: uncertain intent, uncertain result [Review of Experimental Design and Data Analysis for Biologists, by G.P. Quinn & M.J. Keough]. Ecology 83, 810812.
  • Hurlbert, S.H. & Lombardi, C.M. (2009). Final collapse of the Neyman-Pearson decision-theoretic framework and rise of the neoFisherian. Ann. Zool. Fenn. 46, 311349.
  • ICH (1999). ICH harmonised tripartite guideline: statistical principles for clinical trials. E9 Expert Working Group, International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use. Stat. Med. 18, 19051942
  • Kaiser, H.F. (1960). Directional statistical decisions. Psych. Rev. 67, 160167.
  • Kendall, M. & Stuart, A. (1979). The Advanced Theory of Statistics, 2, Inference and Relationship, 4th edn. London : Griffin.
  • Keppel, G. (1991). Design and Analysis: A Researcher's Handbook, 3rd edn. Englewood Cliffs, NJ : Prentice Hall.
  • Kimmel, H.D. (1957). Three criteria for the use of one-tailed tests. Psych. Bull. 16, 345353.
  • Kirk, R.E. (1982). Experimental Design, 2nd edn. Pacific Grove , CA : Brooks/Cole Publishing.
  • Kline, R.B. (2004). Beyond Significance Testing. Washington , DC : American Psychological Association.
  • Kornilov, S.G. (1993). Errors in the description of the F test and some thoughts on one-sided statistical tests. Industr. Lab. 59, 720725. (Translation of Russian article published in: Zavodskaya Laboratoriya 59 , 60, 1993).
  • Leek, J.T. & Storey, J.D. (2008). A general framework for multiple testing dependence. Proc. Natl. Acad. Sci. USA 105, 1871818723.
  • Little, T.M. (1978). If Galileo published in HortScience. HortScience 13, 504506.
  • Lombardi, C.M. & Hurlbert, S.H. (2009). Misprescription and misuse of one-tailed tests. Austral Ecol. 34, 447468.
  • Mantel, N. (1983). Ordered alternatives and the 1–1/2 tail test. Amer. Statist. 37, 225228.
  • Marcus, R., Peritz, E. & Gabriel, K.R. (1976). On closed testing procedures with special reference to ordered analysis of variance. Biometrika 63, 655660.
  • Martin, P. & Bateson, P. (2007). Measuring Behaviour: An Introductory Guide, 3rd edn. Cambridge : Cambridge University Press.
  • Mead, R. (1988). The Design of Experiments. Cambridge : Cambridge University Press.
  • Mead R. & Curnow, R.N. (1983). Statistical Methods in Agriculture and Experimental Biology. New York : Chapman and Hall.
  • Mead, R., Curnow, R.N. & Hasted, A.M. (2003). Statistical Methods in Agriculture and Experimental Biology, 3rd edn. New York : Chapman & Hall.
  • Meek, G.E. & Ozgur, C.O. (2004). Unequal division of type I risk in statistical inferences. Decision Sci. J. Innov. Educ. 2, 4555.
  • Meek, G.E. & Turner, S.J. (1983). Statistical Analysis for Business Decisions. Upper Saddle River , NJ : Houghton and Mifflin.
  • Miller, R.G., Jr. (1981). Simultaneous Statistical Inference, 2nd edn. New York : Springer.
  • Moore, D.S. & McCabe, G.P. (1989). Introduction to the Practice of Statistics. New York : W.H. Freeman.
  • Moran, M.D. (2003). Arguments for rejecting the sequential Bonferroni in ecological studies. Oikos 100, 403405.
  • Moyé, L.A. (2000). Statistical Reasoning in Medicine: The Intuitive P-value Primer. New York : Springer.
  • Moyé, L.A. (2003). Multiple Analyses in Clinical Trials: Fundamentals for Investigators. New York : Springer.
  • Moyé, L.A. (2006a). Statistical Monitoring of Clinical Trials. New York : Springer.
  • Moyé, L.A. (2006b). Statistical Reasoning in Medicine: The Intuitive P-value Primer, 2nd edn. New York : Springer.
  • Moyé, L.A. (2008). Disciplined analyses in clinical trials: The dark heart of the matter. Stat. Meth. Med. Res. 17, 253264.
  • Nakagawa, S. (2004). A farewell to Bonferroni: the problems of low statistical power and publication bias. Behav. Ecol. 15, 10441045.
  • Neale, M.C. & Miller, M.B. (1996). The use of likelihood-based confidence intervals in genetic models. Behav. Genet. 27, 113120.
  • Neuhaus, K.-L., Von Essen, R., Tebbe, U., Vogt, A., Roth, M., Reiss, M., Niederer, W., Forycki, F., Wirtzfeld, A., Maeurer, W., Limbourg, P., Merx, W. & Haerten, K. (1992). Improved thrombolysis in acute myocardial infarction with front-loaded administration of Alteplase: results of the rt-PA–APSAC patency study (TAPS). J. Amer. Coll. Cardiol. 19, 885891.
  • Nickerson, R.S. (2000). Null hypothesis significance testing: A review of an old and continuing controversy. Psych. Meth. 5, 241301.
  • Nosanchuk, T.A. (1978). Serendipity tails: a note on two tailed hypothesis tests with asymmetric regions of rejection. Acta Sociol., 21, 249253.
  • Oakes, M. (1986). Statistical Inference: A Commentary for the Social and Behavioural Sciences. New York : Wiley.
  • O’Brien, P.C. (1983). The appropriateness of analysis of variance and multiple-comparison procedures. Biometrics 93, 787788.
  • O’Keefe, D.J. (2003). Colloquy: Should familywise alpha be adjusted? Against familywise alpha adjustment. Human Commun. Res. 29, 431447.
  • O’Neill, R. & Wetherill, G.B. (1971). The present state of multiple comparison methods. J. Roy. Stat. Soc. Ser. B Stat. Methodol. 33, 218250.
  • Pearce, S.C. (1993). Data analysis in agricultural experimentation. III. Multiple comparisons. Exper. Agric. 29, 18.
  • Perneger, T.V. (1998). What's wrong with Bonferroni adjustments. Brit. Med. J. 316, 12361238.
  • Perry, J.N. (1986). Multiple-comparison procedures: a dissenting view. J. Econ. Entomol. 79, 11491155.
  • Pillemer, D.B. (1991). One- versus two-tailed hypothesis tests in contemporary educational research. Educ. Researcher 20(9), 1317.
  • Pocock, S.J. (1997). Clinical trials with multiple outcomes: a statistical perspective on their design, analysis, and interpretation. Controll. Clin. Trials 18, 530545.
  • Preece, D.A. (1982). The design and analysis of experiments: What has gone wrong? Util. Math. 21A, 210244.
  • Quinn, G.P. & Keough, M.J. (2002). Experimental Design and Data Analysis for Biologists. Cambridge : Cambridge University Press.
  • Ramsey, P.H. (1990). ’One-and-a-half-tailed’ tests of significance. Psych. Reports 66, 653654.
  • Rice, W.R. & Gaines, S.D. (1994a). Extending nondirectional heterogeneity tests to evaluate simple ordered alternative hypotheses. Proc. Natl. Acad. Sci. USA 91, 225226.
  • Rice, W.R. & Gaines, S.D. (1994b). The ordered heterogeneity family of tests. Biometrics 50, 746752.
  • Rice, W.R. & Gaines, S.D. (1994c). ‘Heads I win, tails you lose’: Testing directional alternative hypotheses in ecological and evolutionary research. Trends Ecol. Evol. 9, 235237.
  • Rothman, K.J. (1990). No adjustments are needed for multiple comparisons. Epidemiology 1, 4346.
  • Saville, D.J. (1990). Multiple comparison procedures: the practical solution. Amer. Statist. 44, 174180.
  • Savitz, D.A. & Olshan, A.F. (1995). Multiple comparisons and related issues in the interpretation of epidemiologic data. Am. J. Epidemiol. 142, 904908.
  • Savitz, D.A. & Olshan, A.F. (1998). Describing data requires no adjustment for multiple comparisons: a reply from Savitz and Olshan. Am. J. Epidemiol. 147, 813.
  • Schulman, R.S. (1992). Statistics in Plain English. New York : Van Nostrand Reinhold.
  • Schulz, K.F. & Grimes, D.A. (2005). Multiplicity in randomized trials I: Endpoints and treatments. Lancet 365, 15911595.
  • Seeger, P. (1968). A note on method for the analysis of significances en masse. Technometrics 10, 586593.
  • Senn, S. & Bretz, F. (2007). Power and sample size when multiple endpoints are considered. Pharm. Statist. 6, 161170.
  • Shaffer, J.P. (1972). Directional statistical hypotheses and comparisons among means. Psych. Bull. 77, 195197.
  • Shaffer, J.P. (1995). Multiple hypothesis testing. Ann. Rev. Psychol. 46, 561584.
  • Shaffer, J.P. (2006). Recent developments towards optimality in multiple hypothesis testing. IMS Lecture Notes–Monograph Series 49, 1632.
  • Siegel, S.,(1956). Nonparametric Statistics for the Behavioral Sciences. New York : McGraw-Hill.
  • Siegel, S. & Castellan, N.J., Jr. (1988) Nonparametric Statistics for the Behavioral Sciences, 2d edn. New York : McGraw-Hill.
  • Singh, A. & Dan, I. (2006). Exploring the false discovery rate in multichannel NIRS. Neuroimage 33, 542549.
  • Skipper, K.S., Jr., Guenther, A.L. & Nass, G. (1967). The sacredness of .05: a note concerning the uses of statistical levels of significance in social science. Amer. Sociol. 2, 1618.
  • Snedecor, G.W. & Cochran, W.G. (1989). Statistical Methods, 8th edn. Ames , IO : Iowa State University Press.
  • Sokal, R.R. & Rohlf, F.J. (1995). Biometry, 3rd edn. San Francisco : Freeman.
  • Soric, B. (1989). Statistical “discoveries” and effect-size estimation. J. Amer. Statist. Assoc. 84, 608610.
  • Soto, D. & Hurlbert, S.H. (1991). Long-term experiments on calanoid-cyclopoid interactions. Ecol. Monogr. 61, 245265.
  • Spanos, A. (1999). Probability Theory and Statistical Inference: Econometric Modeling with Observational Data. Cambridge : Cambridge University Press.
  • Spanos, A. (2008). Review of The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice and Lives, by S.T. Ziliak and D.N. McCloskey. Erasmus J. Philos. Econ. 1, 154164.
  • Spjøtvoll, E. (1972). On the optimality of some multiple comparison procedures. Ann. Math. Statist. 43, 398411.
  • Stewart-Oaten, A. (1995). Rules and judgments in statistics: three examples. Ecology 76, 20012009.
  • Storey, J.D. (2003). The positive false discovery rate: A Bayesian interpretation and the q-value. Ann. Statist. 6, 20132035.
  • Storey, J.D. (2007). The optimal discovery procedure: a new approach to simultaneous significance testing. J. R. Stat. Soc. Ser. B Stat. Methodol. 69, 347368.
  • Storey, J.D., Taylor, J.E. & Siegmund, D. (2004). Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates: A unified approach. J. R. Stat. Soc. Ser. B Stat. Methodol. 66, 187205.
  • Storey, J.D., Dai, J.Y. & Leek, J.T. (2007). The optimal discovery procedure for large-scale significance testing, with applications to comparative microarray experiments. Biostatistics 8, 414432.
  • Tukey, J.W. (1953). The problem of multiple comparisons. The Collected Works of John W. Tukey, Volume III, ed. H.I. Braun, pp. 1300 [1994]. New York : Chapman and Hall. [Work completed and privately circulated starting in 1953.
  • Tukey, J.W. (1977). Some thoughts on clinical trials, especially problems of multiplicity. Science, 198, 679684.
  • Tukey, J.W. (1991). The philosophy of multiple comparisons. Stat. Sci. 6, 100116.
  • Underwood, A.J. (1997). Experiments in Ecology. London : Blackwell.
  • Verhoeven, K.J.F. (2005). Implementing false discovery rate control: increasing your power. Oikos 108, 643647.
  • Welkowitz, J., Ewen, R.B. & Cohen, J. (1971, 1991, 1999). Introductory Statistics for the Behavioral Sciences, 1st, 4th, 5th edns. New York : Harcourt Brace Jovanovich.
  • Welkowitz, J., Cohen, B.H. & Ewen, R.B. (2006). Introductory Statistics for the Behavioral Sciences, 6th edn. New York : Wiley.
  • Westfall, P.H. & Krishen, A. (2001). Optimally weighted, fixed sequence and gatekeeper multiple testing procedures. J. Statist. Plann. Inference 99, 2540.
  • Westfall, P.H. & Young, S.S. (1993). Resampling-Based Multiple Testing: Examples and Methods for p-value Adjustment. New York : Wiley.
  • Wilson, W. (1962). A note on the inconsistency inherent in the necessity to perform multiple comparisons. Psych. Bull. 59, 296300.
  • Yates, R. & Healy, M.J.R. (1964). How should we reform the teaching of statistics? J. Roy. Statist. Soc. Ser. A 127, 199210.
  • Zar, J.H. (2004) Biostatistical Analysis, 4th edn. New York : Prentice-Hall, Inc.
  • Ziliak, S.T. & McCloskey, D.N. (2008). The Cult of Statistical Significance: How the Standard Error Cost Us Jobs, Justice and Lives. Ann Arbor , MI : University of Michigan Press.