Improving Transparency, Falsifiability, and Rigor by Making Hypothesis Tests Machine-Readable

Making scientific information machine-readable greatly facilitates its reuse. Many articles have the goal to test a hypothesis, so making tests of statistical predictions easier find and access could be very beneficial. We propose an approach that can used make hypothesis machine-readable. believe there are two benefits specifying in such way computer evaluate whether prediction is corroborated or not. First, become more transparent, falsifiable, rigorous. Second, scientists benefit if related easily findable reusable, for example, perform meta-analyses, conduct peer review, examine metascientific research questions. what should look like demonstrate feasibility real-life example using fully operational prototype R package scienceverse.