Second section: "Data analysis" found within the verses - Problem formulation and exploration (Prov 25:2) - "Search out" implies systematic collection, cleaning, and modeling to uncover hidden structure. Analysts operationalize this through exploratory data analysis, feature engineering, and triangulation across data sources. The verse also implies epistemic humility: we expect concealed structure and therefore test assumptions, use holdout data, and perform robustness checks. - Measurement validity and fairness (Prov 11:1) - "Accurate weights" maps to valid instruments, calibrated sensors, and welldefined metrics. In statistics, this includes reliability analyses, measurement invariance, and error estimation. Ethically, it points to fairness checks to avoid "dishonest scales" embedded in algorithms (e.g., proxies that disadvantage groups). Mittelstadt et al. (2016) show that opacity and bias in algorithmic systems can harm stakeholdersexactly the concern of this proverb. - Transparency and reproducibility (both verses) - Honest "weights" require methods that others can verify. Reproducible pipelines, versioncontrolled code, and preregistration counter practices like undisclosed analytic flexibility, which Simmons, Nelson, and Simonsohn (2011) showed can inflate falsepositive rates. Peng (2011) argues reproducibility is the "minimum standard" for computational sciencean applied form of truthful scales. In human written words commen sense