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.

Software

This book is built with R (R Core Team, 2026b) and Quarto, using the knitr engine (Xie, 2025) to run and typeset live R code. Throughout the book, a number of R packages beyond base R are used to demonstrate specific functionality: BayesFactor (Morey & Rouder, 2026), car (Fox, Weisberg, & Price, 2026), effects (Fox, Weisberg, Price, Friendly, et al., 2026), foreign (R Core Team, 2026a), gdata (Warnes et al., 2024), gplots (Warnes et al., 2025), HistData (Friendly et al., 2025), lmtest (Hothorn et al., 2022), lsr (Navarro, 2026), mvtnorm (Genz et al., 2026), psych (Revelle, 2026), R.matlab (Bengtsson, 2022), scatterplot3d (Ligges et al., 2026), and sciplot (Morales et al., 2020).

Academic sources

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.

Amrhein, V., Greenland, S., & McShane, B. (2019). Retire statistical significance. Nature, 567, 305–307.

Anscombe, F. J. (1973). Graphs in statistical analysis. American Statistician, 27, 17–21.

Bengtsson, H. (2022). R.matlab: Read and write MAT files and call MATLAB from within r. https://github.com/HenrikBengtsson/R.matlab

Benjamin, D. J., Berger, J. O., Johannesson, M., Nosek, B. A., Wagenmakers, E.-J., Berk, R., Bollen, K. A., et al. (2018). Redefine statistical significance. Nature Human Behaviour, 2(1), 6–10.

Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B, 57(1), 289–300.

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). Science and statistics. 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 the equality of variances. Journal of the American Statistical Association, 69, 364–367.

Bürkner, P.-C. (2017). Brms: An R package for Bayesian multilevel models using Stan. Journal of Statistical Software, 80(1), 1–28.

Campbell, D. T., & Stanley, J. C. (1963). Experimental and quasi-experimental designs for research. Houghton Mifflin.

Champely, S. (2020). Pwr: Basic functions for power analysis. https://github.com/heliosdrm/pwr

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.

Cumming, G. (2014). The new statistics: Why and how. Psychological Science, 25(1), 7–29. https://doi.org/10.1177/0956797613504966

Devezer, B., Navarro, D. J., Vandekerckhove, J., & Buzbas, E. O. (2021). The case for formal methodology in scientific reform. Royal Society Open Science, 8(3), 200805. https://doi.org/10.1098/rsos.200805

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.

Fox, J., Weisberg, S., & Price, B. (2026). Car: Companion to applied regression. https://github.com/bprice2652/car_repo

Fox, J., Weisberg, S., Price, B., Friendly, M., & Hong, J. (2026). Effects: Effect displays for linear, generalized linear, and other models. https://cran.r-project.org/package=effects

Friendly, M. (2011). HistData: Data sets from the history of statistics and data visualization. http://CRAN.R-project.org/package=HistData

Friendly, M., Dray, S., Li, P., & Bellhouse, D. (2025). HistData: Data sets from the history of statistics and data visualization. https://friendly.github.io/HistData/

Gelman, A., & Stern, H. (2006). The difference between “significant” and “not significant” is not itself statistically significant. The American Statistician, 60, 328–331.

Genz, A., Bretz, F., Miwa, T., Mi, X., & Hothorn, T. (2026). Mvtnorm: Multivariate normal and t distributions. https://codeberg.org/thothorn/mvtnorm

Gunel, E., & Dickey, J. (1974). Bayes factors for independence in contingency tables. Biometrika, 545–557.

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference and prediction (2nd ed). Springer.

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.

Hoekstra, R., Morey, R. D., Rouder, J. N., & Wagenmakers, E.-J. (2014). Robust misinterpretation of confidence intervals. Psychonomic Bulletin & Review, 21(5), 1157–1164. https://doi.org/10.3758/s13423-013-0572-3

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.

Hothorn, T., Zeileis, A., Farebrother, R. W., & Cummins, C. (2022). Lmtest: Testing linear regression models.

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.

John, L. K., Loewenstein, G., & Prelec, D. (2012). Measuring the prevalence of questionable research practices with incentives for truth telling. Psychological Science, 23(5), 524–532.

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. (2015). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan (2nd ed.). 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.

Lakens, D. (2013). Calculating and reporting effect sizes to facilitate cumulative science: A practical primer for t-tests and ANOVAs. Frontiers in Psychology, 4, 863. https://doi.org/10.3389/fpsyg.2013.00863

Lakens, D., & Caldwell, A. R. (2021). Simulation-based power analysis for factorial analysis of variance designs. Advances in Methods and Practices in Psychological Science, 4(1). https://doi.org/10.1177/2515245920951503

Langsrud, Ø. (2003). ANOVA for unbalanced data: Use type II instead of type III sums of squares. Statistics and Computing, 13(2), 163–167. https://doi.org/10.1023/A:1023260610025

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.

Ligges, U., Maechler, M., & Schnackenberg, S. (2026). scatterplot3d: 3D scatter plot.

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.

Morales, M., R Development Core Team, with code developed by the, R-help listserv community, with general advice from the, & Duncan Murdoch., especially. (2020). Sciplot: Scientific graphing functions for factorial designs.

Morey, R. D., & Davis-Stober, C. P. (2026). On the poor statistical properties of the p-curve meta-analytic procedure. Journal of the American Statistical Association, 121(553), 741–753. https://doi.org/10.1080/01621459.2025.2544397

Morey, R. D., Hoekstra, R., Rouder, J. N., Lee, M. D., & Wagenmakers, E.-J. (2016). The fallacy of placing confidence in confidence intervals. Psychonomic Bulletin & Review, 23(1), 103–123. https://doi.org/10.3758/s13423-015-0947-8

Morey, R. D., & Rouder, J. N. (2015). BayesFactor: Computation of bayes factors for common designs. http://CRAN.R-project.org/package=BayesFactor

Morey, R. D., & Rouder, J. N. (2026). BayesFactor: Computation of bayes factors for common designs. https://richarddmorey.github.io/BayesFactor/

Munafò, M. R., Nosek, B. A., Bishop, D. V. M., Button, K. S., Chambers, C. D., Percie du Sert, N., Simonsohn, U., Wagenmakers, E.-J., Ware, J. J., & Ioannidis, J. P. A. (2017). A manifesto for reproducible science. Nature Human Behaviour, 1(1), 0021.

Navarro, D. (2026). Lsr: Companion to "learning statistics with r". https://github.com/djnavarro/lsr

Navarro, D. J. (2019). Between the devil and the deep blue sea: Tensions between scientific judgement and statistical model selection. Computational Brain & Behavior, 2, 28–34.

Nosek, B. A., Alter, G., Banks, G. C., Borsboom, D., Bowman, S. D., Breckler, S. J., Buck, S., Chambers, C. D., Chin, G., Christensen, G., et al. (2015). Promoting an open research culture. Science, 348(6242), 1422–1425.

Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349(6251), aac4716.

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, Tran.). Henry Holt.

R Core Team. (2026a). Foreign: Read data stored by minitab, s, SAS, SPSS, stata, systat, weka, dBase, ... https://svn.r-project.org/R-packages/trunk/foreign/

R Core Team. (2026b). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://doi.org/10.32614/R.manuals

Revelle, W. (2026). Psych: Procedures for psychological, psychometric, and personality research. https://personality-project.org/r/psych/

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.

Schoot, R. van de, Depaoli, S., King, R., Kramer, B., Märtens, K., Tadesse, M. G., Vannucci, M., Gelman, A., Veen, D., Willemsen, J., & Yau, C. (2021). Bayesian statistics and modelling. Nature Reviews Methods Primers, 1, 1.

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.

Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011). False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological Science, 22(11), 1359–1366.

Simonsohn, U., Nelson, L. D., & Simmons, J. P. (2014). P-curve: A key to the file-drawer. Journal of Experimental Psychology: General, 143(2), 534–547. https://doi.org/10.1037/a0033242

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.

Steegen, S., Tuerlinckx, F., Gelman, A., & Vanpaemel, W. (2016). Increasing transparency through a multiverse analysis. Perspectives on Psychological Science, 11(5), 702–712.

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.

Stroebe, W., Postmes, T., & Spears, R. (2012). Scientific misconduct and the myth of self-correction in science. Perspectives on Psychological Science, 7(6), 670–688.

Student, A. (1908). The probable error of a mean. Biometrika, 6, 1–2.

Teetor, P. (2011). R cookbook. O’Reilly.

Warnes, G. R., Bolker, B., Bonebakker, L., Gentleman, R., Huber, W., Liaw, A., Lumley, T., Maechler, M., Magnusson, A., Moeller, S., Schwartz, M., Venables, B., & Galili, T. (2025). Gplots: Various r programming tools for plotting data. https://github.com/talgalili/gplots

Warnes, G. R., Gorjanc, G., Magnusson, A., Andronic, L., Rogers, J., MacQueen, D., & Korosec, A. (2024). Gdata: Various r programming tools for data manipulation. https://github.com/r-gregmisc/gdata

Wasserstein, R. L., & Lazar, N. A. (2016). The ASA’s statement on p-values: Context, process, and purpose. The American Statistician, 70(2), 129–133.

Wasserstein, R. L., Schirm, A. L., & Lazar, N. A. (2019). Moving to a world beyond "p < 0.05". The American Statistician, 73(sup1), 1–19.

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.

Whittingham, M. J., Stephens, P. A., Bradbury, R. B., & Freckleton, R. P. (2006). Why do we still use stepwise modelling in ecology and behaviour? Journal of Animal Ecology, 75(5), 1182–1189. https://doi.org/10.1111/j.1365-2656.2006.01141.x

Wickham, H. (2007). Reshaping data with the reshape package. Journal of Statistical Software, 21.

Wickham, H. (2014). Tidy data. Journal of Statistical Software, 59(10), 1–23. https://doi.org/10.18637/jss.v059.i10

Wickham, H. (2016). ggplot2: Elegant graphics for data analysis (2nd ed.). Springer. https://doi.org/10.1007/978-3-319-24277-4

Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Pedersen, T. L., Miller, E., Bache, S. M., Müller, K., Ooms, J., Robinson, D., Seidel, D. P., Spinu, V., … Yutani, H. (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686. https://doi.org/10.21105/joss.01686

Wickham, H., & Grolemund, G. (2017). R for data science: Import, tidy, transform, visualize, and model data. O’Reilly Media.

Wilkinson, L., Wills, D., Rope, D., Norton, A., & Dubbs, R. (2006). The grammar of graphics. Springer.

Xie, Y. (2025). Knitr: A general-purpose package for dynamic report generation in r. https://yihui.org/knitr/

Yates, F. (1934). Contingency tables involving small numbers and the χ² test. Supplement to the Journal of the Royal Statistical Society, 1, 217–235.