References

Author’s note – I’ve mentioned it before, but I’ll quickly mention it again. This reference list is appallingly incomplete. Please don’t assume that these are the only sources I’ve relied upon. The final version of this book will have a lot more references. And if you see anything clever sounding in this book that doesn’t seem to have a reference, I can absolutely promise you that the idea was someone else’s. This is an introductory textbook: none of the ideas are original. I’ll take responsibility for all the errors, but I can’t take credit for any of the good stuff. Everything smart in this book came from someone else, and they all deserve proper attribution for their excellent work. I just haven’t had the chance to give it to them yet.

Adair, G. (1984). The hawthorne effect: A reconsideration of the methodological artifact. Journal of Applied Psychology, 69, 334–345.
Agresti, A. (1996). An introduction to categorical data analysis. Wiley.
Agresti, A. (2002). Categorical data analysis (2nd ed.). Wiley.
Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19, 716–723.
Anscombe, F. J. (1973). Graphs in statistical analysis. American Statistician, 27, 17–21.
Bickel, P. J., Hammel, E. A., & O’Connell, J. W. (1975). Sex bias in graduate admissions: Data from Berkeley. Science, 187, 398–404.
Box, G. E. P. (1976). Journal of the American Statistical Association, 71, 791–799.
Box, J. F. (1987). Guinness, gosset, fisher, and small samples. Statistical Science, 2, 45–52.
Braun, J., & Murdoch, D. J. (2007). A first course in statistical programming with R. Cambridge University Press Cambridge.
Brown, M. B., & Forsythe, A. B. (1974). Robust tests for equality of variances. Journal of the American Statistical Association, 69, 364–367.
Campbell, D. T., & Stanley, J. C. (1963). Experimental and quasi-experimental designs for research. Houghton Mifflin.
Cochran, W. G. (1954). The χ2 test of goodness of fit. The Annals of Mathematical Statistics, 23, 315–345.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum.
Cook, R. D., & Weisberg, S. (1983). Diagnostics for heteroscedasticity in regression. Biometrika, 70, 1–10.
Cramér, H. (1946). Mathematical methods of statistics. Princeton University Press.
Dunn, O. J. (1961). Multiple comparisons among means. Journal of the American Statistical Association, 56, 52–64.
Ellis, P. D. (2010). The essential guide to effect sizes: Statistical power, meta-analysis, and the interpretation of research results. Cambridge University Press.
Ellman, M. (2002). Soviet repression statistics: Some comments. Europe-Asia Studies, 54(7), 1151–1172.
Evans, J. St. B. T., Barston, J. L., & Pollard, P. (1983). On the conflict between logic and belief in syllogistic reasoning. Memory and Cognition, 11, 295–306.
Evans, M., Hastings, N., & Peacock, B. (2011). Statistical distributions (3rd ed). Wiley.
Fisher, R. A. (1922a). On the interpretation of χ2 from contingency tables, and the calculation of p. Journal of the Royal Statistical Society, 84, 87–94.
Fisher, R. A. (1922b). On the mathematical foundation of theoretical statistics. Philosophical Transactions of the Royal Society A, 222, 309–368.
Fisher, R. A. (1925). Statistical methods for research workers. Oliver; Boyd.
Fox, J., & Weisberg, S. (2011). An R companion to applied regression (2nd ed.). Sage.
Friendly, M. (2011). HistData: Data sets from the history of statistics and data visualization. http://CRAN.R-project.org/package=HistData
Gelman, A., & Stern, H. (2006). The difference between “significant” and “not significant” is not itself statistically significant. The American Statistician, 60, 328–331.
Gunel, E., & Dickey, J. (1974). Bayes factors for independence in contingency tables. Biometrika, 545–557.
Hays, W. L. (1994). Statistics (5th ed.). Harcourt Brace.
Hedges, L. V. (1981). Distribution theory for glass’s estimator of effect size and related estimators. Journal of Educational Statistics, 6, 107–128.
Hedges, L. V., & Olkin, I. (1985). Statistical methods for meta-analysis. Academic Press.
Hogg, R. V., McKean, J. V., & Craig, A. T. (2005). Introduction to mathematical statistics (6th ed.). Pearson.
Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6, 65–70.
Hothersall, D. (2004). History of psychology. McGraw-Hill.
Hsu, J. C. (1996). Multiple comparisons: Theory and methods. Chapman; Hall.
Ioannidis, J. P. A. (2005). Why most published research findings are false. PLoS Med, 2(8), 697–701.
Jeffreys, H. (1961). The theory of probability (3rd ed.). Oxford.
Johnson, V. E. (2013). Revised standards for statistical evidence. Proceedings of the National Academy of Sciences, (48), 19313–19317.
Kahneman, D., & Tversky, A. (1973). On the psychology of prediction. Psychological Review, 80, 237–251.
Kass, R. E., & Raftery, A. E. (1995). Bayes factors. Journal of the American Statistical Association, 90, 773–795.
Keynes, J. M. (1923). A tract on monetary reform. Macmillan; Company.
Kruschke, J. K. (2011). Doing Bayesian data analysis: A tutorial with R and BUGS. Academic Press.
Kruskal, W. H., & Wallis, W. A. (1952). Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association, 47, 583–621.
Kühberger, A., Fritz, A., & Scherndl, T. (2014). Publication bias in psychology: A diagnosis based on the correlation between effect size and sample size. Public Library of Science One, 9, 1–8.
Larntz, K. (1978). Small-sample comparisons of exact levels for chi-squared goodness-of-fit statistics. Journal of the American Statistical Association, 73, 253–263.
Lee, M. D., & Wagenmakers, E.-J. (2014). Bayesian cognitive modeling: A practical course. Cambridge University Press.
Lehmann, E. L. (2011). Fisher, Neyman, and the creation of classical statistics. Springer.
Levene, H. (1960). Robust tests for equality of variances. In I. O. et al (Ed.), Contributions to probability and statistics: Essays in honor of harold hotelling (pp. 278–292). Stanford University Press.
Long, J. S., & Ervin, L. H. (2000). Using heteroscedasticity consistent standard errors in thee linear regression model. The American Statistician, 54, 217–224.
Matloff, N., & Matloff, N. S. (2011). The art of R programming: A tour of statistical software design. No Starch Press.
McGrath, R. E., & Meyer, G. J. (2006). When effect sizes disagree: The case of r and d. Psychological Methods, 11, 386–401.
McNemar, Q. (1947). Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika, 12, 153–157.
Meehl, P. H. (1967). Theory testing in psychology and physics: A methodological paradox. Philosophy of Science, 34, 103–115.
Morey, R. D., & Rouder, J. N. (2015). BayesFactor: Computation of bayes factors for common designs. http://CRAN.R-project.org/package=BayesFactor
Pearson, K. (1900). On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Philosophical Magazine, 50, 157–175.
Pfungst, O. (1911). Clever hans (the horse of mr. Von osten): A contribution to experimental animal and human psychology (C. L. Rahn, Trans.). Henry Holt.
R Core Team. (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing.
Rosenthal, R. (1966). Experimenter effects in behavioral research. Appleton.
Rouder, J. N., Speckman, P. L., Sun, D., Morey, R. D., & Iverson, G. (2009). Bayesian t-tests for accepting and rejecting the null hypothesis. Psychonomic Bulletin & Review, 16, 225–237.
Sahai, H., & Ageel, M. I. (2000). The analysis of variance: Fixed, random and mixed models. Birkhauser.
Shaffer, J. P. (1995). Multiple hypothesis testing. Annual Review of Psychology, 46, 561–584.
Shapiro, S. S., & Wilk, M. B. (1965). An analysis of variance test for normality (complete samples). Biometrika, 52, 591–611.
Sokal, R. R., & Rohlf, F. J. (1994). Biometry: The principles and practice of statistics in biological research (3rd ed.). Freeman.
Spector, P. (2008). Data manipulation with R. Springer.
Stevens, S. S. (1946). On the theory of scales of measurement. Science, 103, 677–680.
Stigler, S. M. (1986). The history of statistics. Harvard University Press.
Student, A. (1908). The probable error of a mean. Biometrika, 6, 1–2.
Teetor, P. (2011). R cookbook. O’Reilly.
Welch, B. L. (1947). The generalization of Student’s” problem when several different population variances are involved. Biometrika, 34, 28–35.
Welch, B. L. (1951). On the comparison of several mean values: An alternative approach. Biometrika, 38, 330–336.
White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrika, 48, 817–838.
Wickham, H. (2007). Reshaping data with the reshape package. Journal of Statistical Software, 21.
Wilkinson, L., Wills, D., Rope, D., Norton, A., & Dubbs, R. (2006). The grammar of graphics. Springer.
Yates, F. (1934). Contingency tables involving small numbers and the χ2 test. Supplement to the Journal of the Royal Statistical Society, 1, 217–235.