Recruiting, Performance Management, and People Analytics

Most of what feels like good judgment about people is noise. The interview that "just clicked," the star rating that says more about the manager than the employee, the dashboard that proves whatever the person who built it already believed. This course is for the moment you stop trusting the hunch and start asking what actually holds up. It teaches HR work as what it really is: predictions and measurements about people, made under bias, uncertainty, and law. So you'll learn what the current evidence says predicts job performance — structured interviews and work samples, not the freewheeling chat, and not the cognitive-test orthodoxy that the field quietly revised downward in 2022. You'll build a hiring process that's fair on purpose: a scorecard before the search, consistent screening, adverse-impact checks at every stage, and an honest grip on the four-fifths rule (a flag, never a legal verdict). Then performance, without the theater. Why ratings are unreliable and what calibration actually fixes. Goals that work versus goals that get gamed. Why forced ranking rose and fell. How to give feedback and run a PIP that's real, not retroactive cover. Then the numbers. Time-to-fill versus time-to-hire, quality of hire, turnover that's worth having — defined correctly, because most metric mistakes are definitional. And the traps that make HR data lie: survivorship bias, Simpson's paradox, base rates, and Goodhart's law, the reason your best KPI turns on you. Finally, the responsible part: judging an AI hiring tool like a skeptical buyer, the privacy limits on employee data, and regulation that changes faster than any syllabus — so you learn the durable posture, not a statute to memorize. No hype. Just the evidence, its limits, and what to do Monday.