Interpreting functional analysis outcomes using automated nonparametric statistical analysis.

Abstract

Current methods employed to interpret functional analysis data include visual analysis and post-hoc visual inspection (PHVI). However, these methods may be biased by dataset complexity, hand calculations, and rater experience. We examined whether an automated approach using nonparametric rank-based statistics could increase the accuracy and efficiency of functional analysis data interpretation. We applied Automated Nonparametric Statistical Analysis (ANSA) to a sample of 65 published functional analyses for which additional experimental evidence was available to verify behavior function. Results showed that exact behavior function agreement between ANSA and the publications authors was 83.1%, exact agreement between ANSA and PHVI was 75.4%, and exact agreement across all 3 methods was 64.6%. These preliminary findings suggest that ANSA has the potential to support the data interpretation process. A web application that incorporates the calculations and rules utilized by ANSA is accessible at https://ansa.shinyapps.io/ansa/.