Monte Carlo Analyses for Single-Case Experimental Designs: An Untapped Resource for Applied Behavioral Researchers and Practitioners
A free shiny app now gives you a Monte Carlo p-value for any single-case graph in under a minute.
01Research in Context
What this study did
Friedel et al. (2022) built a free shiny app. It runs Monte Carlo tests on single-case graphs.
You paste in your data. The app gives you a p-value that fits behavior-analytic logic.
What they found
The tool works. You can now add an objective number to your visual analysis in minutes.
No new data were collected. The paper shows how to use the app and interpret the output.
How this fits with other research
Dowdy et al. (2022) found most BCBAs still eyeball graphs. Friedel gives them a simple stat fix.
Kranak et al. (2022) offered ANSA for functional analyses. Friedel does the same for treatment graphs.
Fradet et al. (2025) turn visual features into effect sizes. Friedel turns the same features into a p-value. Both keep the graph center stage.
Young (2019) pushed Bayesian tools for small N. Friedel keeps things frequentist and click-button simple.
Why it matters
Next time you run a single-case study, open the shiny app. Enter your baseline and treatment data. Report the Monte Carlo p-value alongside your visual call. Reviewers like numbers and you keep behavior-analytic logic intact.
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02At a glance
03Original abstract
Group-based experimental designs are an outgrowth of the logic of null-hypothesis significance testing and thus, statistical tests are often considered inappropriate for single-case experimental designs. Behavior analysts have recently been more supportive of efforts to include appropriate statistical analysis techniques to evaluate single-case experimental design data. One way that behavior analysts can incorporate statistical analyses into their practices with single-case experimental designs is to use Monte Carlo analyses. These analyses compare experimentally obtained behavioral data to simulated samples of behavioral data to determine the likelihood that the experimentally obtained results occurred due to chance (i.e., a p value). Monte Carlo analyses are more in line with behavior analytic principles than traditional null-hypothesis significance testing. We present an open-source Monte Carlo tool, created in shiny, for behavior analysts who want to use Monte Carlo analyses in addition as part of their data analysis.
Perspectives on Behavior Science, 2022 · doi:10.1007/s40614-021-00318-7