I-Corps: Interactive Statistical Decision Trees for Application in Real-world Contexts

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The broader impact/commercial potential of this I-Corps project involves improvement in the time and cost associated with training needed to prepare data analysts working in research fields outside of academia. Knowledge of statistical methods often does not translate to application to current research questions. The proposed project aims to reduce the time corporations and large and small organizations need in training new data analyst employees. Additionally, the project aims to reduce errors in judgment regarding the application of appropriate statistical tests to particular problems. Commercially, the project may replace libraries of statistical reference manuals utilized to make decisions about application of statistical models.This I-Corps project leverages academic research in statistics to application in real-world research contexts such as data analysis in medical research centers. The project is designed to assist quantitative data analysts in moving from hypothesis to interpretation of statistical results through a simple series of decision steps, using parameters known to analysts across a variety of contexts. The prototype was built using a puzzle/maze typology. Current market available tools are limited in scope of statistical tests, and do not provide demonstration of the test in action with hypothetical data. The current project prototype covers 28 different parametric and non-parametric statistical tests appropriate across all four quantitative data types: categorical, ordinal, interval, and ratio-level data. Additionally, the current prototype provides demonstration of all 28 tests via video-link using sample data, provide output results, and demonstration of appropriate reporting.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.