So is your data really telling you a story, or is it pulling you into an expensive illusion? Let’s take a close look at this “dangerous” but equally exciting topic.
Correlation, or just a coincidence?
At its simplest, correlation means two variables move together. If B rises (or falls) as A rises, we can say there is a correlation between them.
But here is the problem: Two events that follow each other do not necessarily have a cause-and-effect relationship.
The famous “pizza and productivity” fallacy
Say you ran an analysis and found that employee productivity rose by 20% on days when pizza was ordered to the office. The correlation is clear: when there’s pizza, there’s productivity.
If you don’t question causation, the fix looks simple: “Let’s hand out pizza every day and watch productivity soar!” But a month later you may find everyone is unhappy and has put on weight. Why? Maybe the pizza was ordered to celebrate the end of a big project. What lifted productivity was not the pizza but the motivation of hitting a goal and a culture of celebrating it. In HR analytics, mistaking correlation for causation is like treating a company with the wrong medicine.
The 3 most common traps in HR data
- Pay and satisfaction Your analysis may show that higher-paid employees are more engaged. But that does not always mean “If we raise pay, engagement will rise.” Perhaps highly engaged people who take initiative got promoted faster and reached higher pay. The cause and effect may run the other way!
- Working from home and performance When we see “remote workers have higher performance scores,” we may jump straight to “Everyone should work from home.” Yet maybe it is simply the disciplined employees who can manage their own time who choose to work from home. The issue may be personal capability, not location.
- Training and sales success Seeing sales rise after training the sales team is great. But could a competitor have left the market at that time, or could there have been a seasonal jump in demand? If you don’t control for outside factors, you end up defending the training budget with a misleading argument.
Learn to ask “why” so data doesn’t mislead you
As an analytical HR professional, asking these three questions whenever a chart lands in front of you changes the game:
- Is there reverse causation? (Does A cause B, or does B cause A?)
- Is there a third variable? (Is there a hidden factor “C” affecting both variables? For example: company culture, an economic crisis, leadership style.)
- Is this just a coincidence? (Is it statistically significant, or is our dataset too small?)
A statistical note: the correlation coefficient (r)
For those curious about the technical side: correlation is usually measured with the coefficient r, which ranges from -1 to +1.
| Coefficient | Meaning |
|---|---|
| r = 1 | Perfect positive relationship. |
| r = 0 | No relationship at all. |
| r = -1 | Perfect negative relationship. |
But remember: even if r is 0.99, it never tells us “A caused B.” It only says “They dance together really well.”
The spirit of data, the common sense of people
HR analytics is not about seeing people as nothing but numbers. It is about using numbers to read people’s stories more accurately. Data tells us “what happened,” but to understand “why it happened,” HR needs its well-known common sense, conversations with employees and in-depth observation.
A strategic decision based on a false correlation can be riskier than using no data at all. Love your data, but stay “human” enough to always approach it with some skepticism.