I am on my way to Athens right now and we are flying through quite turbulence. Nevertheless, the lulls of the plane and the voice of Nassim Taleb in my ear make me question the enterprise of people analytics, asking a question: Is People Analytics Really Science?
Specifically, the tidal wave of "evidence-based" HR and now AI products flooding the market.
Every week, a new consulting firm drops a white paper claiming their proprietary AI model predicts attrition with 95% accuracy. Or a vendor pitches a new personality assessment guaranteed to identify top-tier leadership potential (that is a whole other can of worms that we can open in another issus).
It looks great. It has charts, footnotes, and percentages.
But is it science? Or, is it scientism?
And confusing the two is a very expensive mistake any leader can make.
The Performance of Science
There is a massive difference between the actual practice of science and the performance of science.
Science produces results by running experiments, testing hypotheses, and building knowledge that holds up under scrutiny.
The end goal is discovery.
The outcome is not guaranteed.
Only the rigorous process is.
Scientism does something completely different.
It borrows the aesthetics of science—the statistics, the peer-sounding terminology, the complex dashboards—to support a conclusion that was already decided.
Usually, because someone has something to sell.
When a researcher runs a multi-wave longitudinal study on employee engagement, controls for industry and tenure, and publishes findings that completely contradict their original hypothesis, that is science.
When a consulting firm releases a white paper titled "The 7 Drivers of Workforce Performance," backed by a survey of 500 self-selected respondents, with no methodology appendix and a massive button to "Book a Demo"—that is scientism.
The conclusion came first. The data second.
The tell is always the motivation.
In theory, science exists for the sake of discovery.
Scientism exists for the sake of sales.
Why People Analytics is Highly Vulnerable
This problem runs incredibly deep in People Analytics.
Our field has a credibility problem it hasn't fully reckoned with. We emerged from two very different worlds: rigorous organizational behavior research, and the tech industry's obsession with data products.
Those two origins do not pull in the same direction. At least not always.
As a result, a massive amount of what gets marketed as "evidence-based HR" is built on "shaky" to say the least ground.
Personality assessments sold as flawless predictors of job performance without measuring performance
Engagement scores packaged as leading indicators of financial outcomes without any evidence of correlations
AI-driven tools making massive causal claims from weak, correlational data, without a control group
None of these are actual science.
They use the aesthetics of rigor, but the underlying methods do not hold up. The validation studies are internal and unpublished. The limitations are never mentioned. The counterfactuals do not exist.
And your sales people, don't even know the details of the data.
Yet, most HR buyers do not have the methodological training to push back.
The CHRO Playbook: Spotting the Difference
If you want to build a truly strategic, high-yield People Analytics function, you have to draw a hard line between a genuine inquiry process and a sales engine.
Before you act on any analytics claim or buy a new piece of software, run it through this playbook:
1. Find the Falsification
Real science makes predictions that could turn out to be wrong. Scientism produces unfalsifiable claims wrapped in data. Ask the vendor: "Under what conditions does your model fail?" If they get defensive, or claim their accuracy is bulletproof, walk away.
2. Demand the Methodology
If the analysis cannot be examined, it cannot be trusted. Do not accept a slick white paper as proof. Demand the raw validation studies. Ask what their false-positive rate is. Ask how they control for environmental variance. If the answer is vague, that is your signal. There were many cases where I challenged the white papers and even helped vendors identify their gaps.
Most of the time, they agree with my points. But their agreement invalidates their conclusions.
There is no sale.
3. Question the "Inside Dataset"
A finding that only exists in one proprietary, closed-door dataset is unscientific. If no one else in the academic or broader analytics community can replicate their claims, you are buying snake oil.
4. Audit Your Own Team (Stop the Justification Engine)
As an analytics practitioner, your job is to find out what is actually true. It is not to build a data-backed case for a decision the CEO already made at an offsite. The moment you start working backward from a conclusion just to appease leadership, you have crossed into scientism yourself.
Do not be fooled by the aesthetics of rigor.
K
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