Descriptive Evidence

Original name

Original description

Use when the deliverable is a DESCRIPTION of what's in the data rather than an effect, a counterfactual, or a prediction — stylized facts, raw and indexed trends ("what's the trend in X", "how has Y changed over time", "is X rising", "plot the growth in …"), summary-statistics / Table-1 tables, distributions, descriptive maps / choropleths (where something is concentrated, how a rate varies across places), and the descriptive-evidence section that motivates an empirical paper. The descriptive layer BENEATH the modeling fork: it is often the deliverable itself, and when it isn't, a stylized fact is what motivates the causal/structural/predictive question (then route to the fork). Forces the comparability choices that silently decide what a fact says — denominator, deflator + base year, per-capita scaling, weighting, unit, window, aggregation level — to be fixed before plotting; makes the signature silent failure LOUD — composition / aggregation artifacts (Simpson's paradox, a shifting denominator or sample, nominal-not-real growth, a mix shift) that make a trend look like a within-group change it isn't; demands a stylized fact be ROBUST to alternative reasonable cuts/definitions/windows or it isn't stylized; and holds the causal firewall — describe in descriptive verbs ("rose alongside", "is higher among"), never causal ones ("raised", "drove", "caused"). Use in R, Julia, or Python whenever someone says "what's the trend", "show me the growth", "summary stats", "Table 1", "describe this data", "stylized facts", "what does the distribution look like", "map where X is concentrated", or "give me some motivating facts" — even when it feels like just a quick plot or map, because a mis-deflated, mis-weighted, composition-driven, or count-not-rate "fact" looks exactly as clean as a real one.