The article argues that inflation expectations cannot be understood from a single headline forecast. In August, New York Fed data showed households’ year-ahead forecasts moving closer together even as the uncertainty within individual forecasts widened. Disagreement and uncertainty therefore capture different things: one measures how far people are from each other, while the other measures how wide a range of outcomes each person considers plausible.

That distinction matters for decisions. People can agree on the central outlook while remaining hesitant because they still see substantial downside risk. For firms, the same logic applies to investment and planning, especially when commitments are costly to reverse. Research can therefore create value without materially changing the headline forecast if it narrows the plausible range enough to alter timing, scale, or reversibility. The piece concludes that forecasts should be discussed alongside the uncertainty around them.

The article argues that research creates value only when information can change a decision, either immediately or through follow-up choices. In a launch example, one customer study is worth $18,000 when favorable and unfavorable results lead to different actions. But when prior confidence is low enough that neither result changes the launch decision, the same study has negative standalone value.

That changes once research becomes sequential. Using backward induction, the model shows that evidence insufficient after one study can still be valuable because it affects whether another study is worth buying. Research therefore has option value, and the optimal stopping point depends on beliefs, signal quality, costs, and available actions. The practical lesson is to define the decision before commissioning more analysis and stop only when further information no longer changes the best action or next worthwhile question.