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Though statistical analyses are centered on research questions andhypotheses, current statistical analysis tools are not. Users must firsttranslate their hypotheses into specific statistical tests and then perform APIcalls with functions and parameters. To do so accurately requires that usershave statistical expertise. To lower this barrier to valid, replicablestatistical analysis, we introduce Tea, a high-level declarative language andruntime system. In Tea, users express their study design, any parametricassumptions, and their hypotheses. Tea compiles these high-level specificationsinto a constraint satisfaction problem that determines the set of validstatistical tests, and then executes them to test the hypothesis. We evaluateTea using a suite of statistical analyses drawn from popular tutorials. We showthat Tea generally matches the choices of experts while automatically switchingto non-parametric tests when parametric assumptions are not met. We simulatethe effect of mistakes made by non-expert users and show that Tea automaticallyavoids both false negatives and false positives that could be produced by theapplication of incorrect statistical tests.

Tea: A High-level Language and Runtime System for Automating Statistical Analysis