A parable on confidence intervals: why "confidence" is misleading

Null hypothesis significance testing (NHST) is increasingly falling out of style with methodologically-minded behavioral and social scientists. Many diverse critiques have been leveled against significance testing; the debate is increasingly what should replace it. Building on work with my colleagues (see here and here), I discuss and critique one replacement option that has been persistently suggested over the years: confidence procedures. We begin with a parable.

New BayesFactor version 0.9.9 released to CRAN

Today I submitted a new release of BayesFactor, version 0.9.9, to CRAN. Among the new features are support for contingency table analyses, via the function contingencyTableBF, and analysis of a single proportion, via the function proportionBF. Other features and fixes include:

• Added "simple" argument to ttest.tstat, oneWayAOV.Fstat, and linearReg.R2stat; when TRUE, return only the Bayes factor (not the log BF and error)
• When sampling Bayes factors, recompute() now increases the precision of BayesFactor objects, rather than simply recomputing them. Precision from new samples is added
• Added Hraba and Grant (1970) data set; see ?raceDolls
• Added model.matrix method for BayesFactor objects; allows for extracting the design matrix used for an analysis
• recompute() now has multicore and callback support, as intended
• Moved many backend functions to Rcpp from R C API
• t test samplers now sample from interval null hypotheses and point null hypotheses where appropriate
• fixed bug in in meta t test sampler which wouldn't allow sampling small numbers of MCMC samples

Embedding RData files in Rmarkdown files for more reproducible analyses

For those of us interested in reproducible analysis, Rmarkdown is a great way of communicating our code to other researchers. Rstudio, in particular, makes it very easy to create attractive HTML document containing text, code, and figures, which can then be sent to colleagues or put on the internet for anyone to see. If you aren't using Rmarkdown for your statistical analyses, I recommend you start; you'll never go back to simple script files again (and your colleagues won't want you to).

In this post, I describe how to improve your Rmarkdown by embedding data that can be downloaded by anyone viewing the document in a modern browser with javascript enabled. For a quick look, see the example Rmd file and resulting HTML file.

BayesFactor version 0.9.8 released to CRAN

BayesFactor version 0.9.8 has been released on CRAN! This is a both a bug fix and feature update. From the NEWS:

•  Fixed bugs in model enumeration code
•  Fixed bug leading to wrong computation of number of covariate when interactions between continuous variables were included
•  Corrected typos/old information in the documentation 
•  Fixed a memory allocation bug that affected computing Bayes factors with lots of data 
•  Added meta-analytic Bayes factor for t tests (see meta.ttestBF)
•  Fixed bug in ttestBF that yielded Bayes factor of NaN for very extreme posterior interval probabilities
•  Fixed several bugs causing infinite integrals; generally improved integration
•  Added check to ensure no missing data before analyses
•  Added callbacks for access by third-party interfaces

See also the new entry in the manual for meta-analytic t tests. In addition to these changes, most of the code for contingency table analysis has been added; these functions will be released in the next update.

BayesFactor update: 0.9.7.

BayesFactor version 0.9.7 has been released to CRAN. It does have a few bug fixes, so update soon. A list of changes can be found in the NEWS file.

Bayes factor t tests, part 2: Two-sample tests

In the previous post, I introduced the logic of Bayes factors for one-sample designs by means of a simple example. In this post, I will give more detail about the models and assumptions used by the BayesFactor package, and also how to do simple analyses of two- sample designs.
See the previous posts for background:

• What is a Bayes factor?
• Bayes factor t tests, part 1

This article will cover two-sample t tests.

Bayes factor t tests, part 1

In my first post, I described the general logic of Bayes factors. I will continue discussing the general logic of Bayes factor, while introducing some of the basic functionality of the BayesFactor package.

What is a Bayes factor?

The BayesFactor package

This blog is a companion to the BayesFactor package in R (website), which supports inference by Bayes factors in common research designs. Bayes factors have been proposed as more principled replacements for common classical statistical procedures such as \(p\) values; this blog will offer tutorials in using the package for data analysis.
In this first post, I describe the general logic of Bayes factors using a very simple research example. In the coming posts, I will show how to do a more complete Bayesian data analysis using the R package.