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演講大綱：

How do we know when apparent agreement reflects genuine consensus? In everyday conversation, professional settings, academic institutions, media, and public life, familiar phrases such as everyone knows, most people think, you can’t go wrong, and studies show often carry more certainty than the evidence behind them warrants. Repetition can resemble agreement, visibility can be mistaken for representation, and confidence can quietly substitute for evidence.

Drawing on themes from the forthcoming book Everyone Says: How We Mistake Confidence for Consensus, co-authored with Chiu-I (Aki) Chen, Professor Jeffrey R. Wilson examines these familiar expressions through the lens of statistical thinking and human decision-making. Rather than presenting statistics through formulas, the lecture uses stories and everyday situations to explore four underlying principles: measurement, variation, representation, and inference.

Through examples involving personal labels, risk and averages, visible versus representative voices, and claims beginning with “studies show,” the lecture demonstrates how simple questions—How many? Compared to what? Who is included? Under what conditions?—can expose assumptions that otherwise remain hidden.

The central argument is not that expertise, intuition, or confident judgment should be rejected. Rather, better decisions emerge when confidence is accompanied by proportion, context, and evidence. The lecture invites participants from different disciplines to consider how statistical reasoning can improve communication, challenge inherited assumptions, and create more thoughtful dialogue in organizations, scholarship, and everyday life.

The session will conclude with an interdisciplinary discussion and Q&A on how these ideas apply across cultures, professions, and fields of study.