Forecasting habits that improve prediction are cheap to install but absent from analytics training

The people analytics field hires for statistics, SQL, storytelling, and business partnership. No job descriptions screen for calibration, for the discipline of competing hypotheses, or for the instinct to seek disconfirming evidence. The field tests whether someone can build the model. It doesn't test whether they know when to distrust it.

The evidence on how cheap the fix is should sting. A Tetlock debiasing module that took under an hour improved real forecasting accuracy by 6-11% and held up across four years. McKinsey reviewed more than a thousand large corporate investments and found that organizations working to strip bias out of decisions earned up to seven percentage points more in returns. This isn't a decade-long capability build.

The barrier isn't cost or complexity. It's that the field's humanistic center of gravity actively resists the cold parts. The research on HR analytics describes practitioners softening or quietly ignoring rigorous output because it threatens how they see their own role. You can't import a discipline the profession experiences as an attack on its identity.

Habit	Cost to install	Evidence
Debiasing module	Under one hour	6-11% accuracy gain, held 4 years (Tetlock)
Calibration drills (90% confidence intervals, scored)	Afternoon	Most analysts tighten quickly after first feedback
Competing hypotheses on high-stakes work	Process norm	Prevents evidence-gathering for the favored story
Outcome-scored post-mortems	Process norm	~2x more effective than process accountability (Tetlock)

Source claim: The habits that separate genuinely good forecasters from confident-but-wrong ones are cheap to install and evidence-backed, but the people analytics field neither selects for them in hiring nor trains them on the job.