Quality Control for Scientific Research: Addressing Reproducibility, Responsiveness, and Relevance

Abstract

Efforts to address a reproducibility crisis have generated several valid proposals for improving the quality of scientific research. We argue there is also need to address the separate but related issues of relevance and responsiveness. To address relevance, researchers must produce what decision makers actually need to inform investments and public policy—that is, the probability that a claim is true or the probability distribution of an effect size given the data. The term responsiveness refers to the irregularity and delay in which issues about the quality of research are brought to light. Instead of relying on the good fortune that some motivated researchers will periodically conduct efforts to reveal potential shortcomings of published research, we could establish a continuous quality-control process for scientific research itself. Quality metrics could be designed through the application of this statistical process control for the research enterprise. We argue that one quality control metric—the probability that a research hypothesis is true—is required to address at least relevance and may also be part of the solution for improving responsiveness and reproducibility. This article proposes a “straw man” solution which could be the basis of implementing these improvements. As part of this solution, we propose one way to “bootstrap” priors. The processes required for improving reproducibility and relevance can also be part of a comprehensive statistical quality control for science itself by making continuously monitored metrics about the scientific performance of a field of research.