Reading Standard Errors Alongside Coefficients

EconometricsCommunicationPolicy

Policy audiences rarely dispute the point estimate on a first read; they challenge whether the range of plausible values still supports a decision. In our blended econometrics courses, we ask participants to draft one paragraph that pairs each headline coefficient with the standard error family used and what changes if the error structure is wrong. This sounds bureaucratic until you watch a cabinet briefing where someone asks what happens if clustering was ignored. Cohorts practice answering without hand-waving, using the same vocabulary they used in week two diagnostics. The goal is not statistical pedantry; it is protecting decision-makers from narratives that are too crisp for the evidence. We also push back on “stars culture.” Stars can be useful as a quick scan, but they are a compression that hides sample design. Participants leave with a personal checklist: data generating process, estimator, error structure, and a plain-language caveat that belongs in the same slide as the coefficient. Finally, we encourage teams to rehearse failure modes. If the error is half as large as assumed, does the recommendation change? If not, say so explicitly. That single sentence often prevents weeks of downstream confusion.