You’ve seen the headlines. "Eating chocolate makes you a Nobel Prize winner!" Or maybe you’ve heard that ice cream sales cause shark attacks because, statistically, they both spike in July. It’s funny. It’s also dangerous. We live in an era where data is basically the new oxygen, but most of us are breathing in fumes because we can't tell the difference between things that happen together and things that cause each other.
Honestly, the short answer is no. Does correlation equal causation? Absolutely not. Not even close. But why do we keep falling for it?
Our brains are hardwired to find patterns. It’s a survival mechanism. If a caveman ate a blue berry and then got a stomach ache, he didn't run a double-blind, peer-reviewed study to see if it was the berry or the dirty water he drank earlier. He just stopped eating the berries. Fast forward a few thousand years, and that same instinct makes us think that because the stock market went up while a certain political party was in power, the party must have "fixed" the economy. This is what's known as cum hoc ergo propter hoc—with this, therefore because of this.
The Spurious Correlation Rabbit Hole
Data can be a liar. A very convincing one.
Tyler Vigen, a Harvard Law student, became internet-famous for his project "Spurious Correlations." He found that the divorce rate in Maine almost perfectly mirrors the per capita consumption of margarine. If you look at the graph, the lines move in total lockstep. It looks like margarine is destroying marriages. But obviously, that's ridiculous. It's a fluke. When you have enough data points, you can find two things that look related even if they have zero connection in the real world.
Then there’s the "Third Variable" problem. This is where a lot of health "science" goes off the rails.
Take the classic example: people who carry lighters are more likely to get lung cancer. If you just looked at the raw data, you might argue that lighters cause cancer. But the lighter isn't the problem. The third variable is smoking. Smokers carry lighters. Smokers get cancer. The lighter is just a "confounder" hanging around at the scene of the crime.
The Gold Standard: How to Actually Prove Cause
So, how do scientists actually prove that A causes B? They use the Randomized Controlled Trial (RCT). This is the "gold standard" you always hear about in medical journals like The Lancet or the New England Journal of Medicine.
👉 See also: Solar Eclipse With Diagram: Why Your Eyes (and Your Camera) Might Be Doing It Wrong
In an RCT, you take a big group of people and split them up randomly. One group gets the "treatment" (the berry, the pill, the new app) and the other gets a placebo. Because the groups are assigned randomly, you've basically neutralized all those weird third variables like age, diet, or how much sleep they got. If the treatment group shows a massive change and the placebo group doesn't, you've finally moved past "hey, these things look related" into the territory of "this thing actually caused that."
But here’s the kicker: RCTs are expensive. Sometimes they’re impossible. You can't randomly assign people to smoke for 20 years just to see if they get cancer. That would be a moral nightmare. In those cases, researchers use things like the Bradford Hill Criteria.
Sir Austin Bradford Hill was a British epidemiologist who, in 1965, laid out a framework to help us decide if a correlation is likely causal. He looked for:
- Strength: How big is the association?
- Consistency: Has this been observed by different people in different places?
- Temporality: Did the "cause" actually happen before the effect? (This sounds simple, but you'd be surprised how often people flip it).
- Biological Gradient: If you do more of the thing, do you get more of the effect? (The "dose-response" relationship).
Why Big Data is Making This Worse
We’re drowning in numbers.
In the tech world, we use A/B testing for everything. If a company changes a button from green to blue and clicks go up 10%, they assume the color caused the click. But was it the color? Or was it because they sent the email at 10:00 AM on a Tuesday when people were bored at work?
Algorithms are the kings of finding correlations. This is why your Netflix or TikTok feed gets so weirdly specific. The algorithm doesn't "know" why you liked a video of a cat playing a piano; it just knows that people who liked that video also liked videos of people making sourdough bread. There’s no causal link—just a massive, invisible web of correlations that machines exploit to keep you scrolling.
The High Cost of Getting It Wrong
This isn't just academic. Real lives get messed up when we confuse these concepts.
In the late 90s, hormone replacement therapy (HRT) was widely prescribed to postmenopausal women. Why? Because observational studies showed that women on HRT had much lower rates of coronary heart disease. It seemed like a miracle. But when a massive RCT was finally done (the Women’s Health Initiative), they found the opposite. HRT actually increased the risk of heart disease for some women.
What happened? The original studies were seeing a correlation. The women who were taking HRT tended to be from higher socioeconomic backgrounds. they ate better, exercised more, and had better healthcare. Those things—not the HRT—were protecting their hearts.
How to Spot a Fake Connection
Next time you see a "study shows" headline, ask yourself three questions.
First, is there a plausible mechanism? If a study says wearing red socks makes you better at math, ask how. Is there a biological or psychological reason? If not, it’s probably a fluke.
Second, look at the sample size. A correlation found in ten people is just a coincidence. A correlation found in ten thousand people is a signal.
Third, look for the "reverse causation" trap. Does exercise make people healthy, or are healthy people more likely to have the energy to exercise? It's usually a bit of both, but we often ignore the loop.
Turning Data into Decisions
Understanding that does correlation equal causation is a "no" should change how you consume information. You stop being a passive recipient of "facts" and start being a skeptic. This doesn't mean you ignore data—it means you respect it enough to question it.
Actionable Insights for Navigating Data:
- Check the Source: Look for peer-reviewed studies over press releases. Press releases are designed to get clicks; journals are (usually) designed to get the truth.
- Identify Confounders: When you see a link between two things, try to brainstorm a "Third Variable." What else could be causing this?
- Demand the "Why": If a correlation doesn't have a logical explanation, treat it as a curious coincidence until proven otherwise.
- Look for Replication: One study is a starting point. Three studies by different teams is a trend. Ten studies is getting close to a fact.
- Control for Variables: If you're running your own tests in business or life, try to change only one thing at a time. If you change your diet, your sleep, and your workout at the same time, you'll never know which one actually worked.
The world is messy. Data helps us clean it up, but only if we use it right. Don't let a pretty graph trick you into thinking you've found the secret to life when all you've found is a lucky coincidence.