Bayesian data analysis as a tool for behavior analysts
Bayesian analysis gives clearer small-sample answers when visual inspection leaves doubt.
01Research in Context
What this study did
Young (2019) wrote a how-to paper, not an experiment. He explains Bayesian data analysis in words a behavior analyst can use.
The paper shows when Bayes helps most: few data points, messy graphs, or models that normal software cannot run.
What they found
Bayesian tools give tighter guesses about true level, trend, and variability when you only have a short baseline or treatment phase.
Traditional visual checks still work for clear effects, but Bayes adds numbers when the picture is fuzzy.
How this fits with other research
Dowdy et al. (2022) found most BCBAs still eyeball graphs and skip structured visual aids. Young offers a number-based partner to those same graphs.
Friedel et al. (2022) handed us a free Monte Carlo p-value app. Both papers give small-N options; Bayes gives a full estimate curve while Monte Carlo gives one p-value.
Tincani et al. (2024) push preregistration to cut bias. Using Bayesian priors fits that move: you state your guess ahead of time, then let the data update it.
Why it matters
If you run single-case sessions and the data path looks flat or jumpy, Bayesian analysis can tell parents and payers how sure you really are. You do not need a stats degree; free programs like JASP or Stan now point and click. Try adding a weak prior to your next five-point baseline and watch the credible interval shrink. The number may convince a supervisor—or an insurer—faster than eyeball overlap alone.
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02At a glance
03Original abstract
Bayesian approaches to data analysis are considered within the context of behavior analysis. The paper distinguishes between Bayesian inference, the use of Bayes Factors, and Bayesian data analysis using specialized tools. Given the importance of prior beliefs to these approaches, the review addresses those situations in which priors have a big effect on the outcome (Bayes Factors) versus a smaller effect (parameter estimation). Although there are many advantages to Bayesian data analysis from a philosophical perspective, in many cases a behavior analyst can be reasonably well-served by the adoption of traditional statistical tools as long as the focus is on parameter estimation and model comparison, not null hypothesis significance testing. A strong case for Bayesian analysis exists under specific conditions: When prior beliefs can help narrow parameter estimates (an especially important issue given the small sample sizes common in behavior analysis) and when an analysis cannot easily be conducted using traditional approaches (e.g., repeated measures censored regression).
Journal of the Experimental Analysis of Behavior, 2019 · doi:10.1002/jeab.512