Proponents of preregistration argue that, among other benefits, it improves the diagnosticity of statistical tests. In the strong version of this argument, preregistration does this by solving statistical problems, such as family-wise error rates. In the weak version, it nudges people to think more deeply about their theories, methods, and analyses. We argue against both: the diagnosticity of statistical tests depend entirely on how well statistical models map onto underlying theories, and so improving statistical techniques does little to improve theories when the mapping is weak. There is also little reason to expect that preregistration will spontaneously help researchers to develop better theories (and, hence, better methods and analyses).
The book is associated with the lsr package on CRAN and GitHub. The package is probably okay for many introductory teaching purposes, but some care is required. The package does have some limitations (e.g., the etaSquared function does strange things for unbalanced ANOVA designs), and it has not been updated in a while.
Learning Statistics with R covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software. The book discusses how to get started in R as well as giving an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book.
This is an archived version of a blog post on preregistration. The first half of the post argues that there is not a strong justification for preregistration as a tool to solve problems with statistical inference (p-hacking); the second half argues that preregistration has a stronger justification as one tool (among many) that can aid scientists in documenting our projects.
Current attempts at methodological reform in sciences come in response to an overall lack of rigor in methodological and scientific practices in experimental sciences. However, some of these reform attempts suffer from the same mistakes and over-generalizations they purport to address. Considering the costs of allowing false claims to become canonized, we argue for more rigor and nuance in methodological reform. By way of example, we present a formal analysis of three common claims in the metascientific literature: (a) that reproducibility is the cornerstone of science; (b) that data must not be used twice in any analysis; and (c) that exploratory projects are characterized by poor statistical practice. We show that none of these three claims are correct in general and we explore when they do and do not hold.