Measure Anything

Apply calibrated estimation techniques from Douglas Hubbards Applied Information Economics. Set 90% confidence intervals, calculate Expected Value of Information, and quantify intangible workforce metrics.

This tool implements Douglas Hubbard's Applied Information Economics (AIE) framework for quantifying uncertain quantities. Users provide 90% confidence intervals — the range within which they believe the true value falls with 90% probability. The tool converts this range into a standard deviation (range / 3.29, since a 90% CI spans approximately 3.29 standard deviations in a normal distribution) and calculates the Expected Value of Information (EVI): how much decision-relevant value would be gained by reducing the uncertainty around each estimate.

EVI is computed by evaluating the probability that the true value crosses a user-defined decision threshold and the magnitude of the potential error from acting on the wrong side of that threshold. High EVI indicates that current uncertainty is large enough to flip a decision — making additional measurement worthwhile. Low EVI means even perfect information wouldn't change what you'd do, so further measurement is waste. The error function (erf) approximation enables the normal CDF calculation needed for threshold-crossing probability.

Practitioners should focus measurement effort on the estimate with highest EVI, not the estimate with widest range or highest dollar value. Most people's initial 90% CIs are systematically overconfident (their "90%" ranges actually contain the true value only 50-60% of the time) — the tool's guidance to widen ranges is critical for calibration. In WFM contexts, this tool is particularly powerful for quantifying "unmeasurables" like the cost of schedule adherence failures, the value of a WFM system upgrade, or the revenue impact of improved service levels. Start with rough estimates; refine only where EVI justifies the measurement cost.