You’ve probably seen the headlines. A new study claims drinking three cups of coffee a day makes you live longer, or a marketing firm swears their new ad campaign "significantly" boosted sales. It sounds definitive. It sounds like science. But honestly, most of the time, people are using the term statistically significant to mean "important" or "massive."
That’s not what it means. Not even close.
In reality, something can be statistically significant and totally useless. On the flip side, something could be life-changingly important but fail to meet the "significant" threshold because the sample size was too small. If you're trying to run a business or understand a medical report, you have to look past the jargon. It’s about probability, not certainty.
What is considered statistically significant anyway?
Basically, statistical significance is a way for researchers to check if a result was just a lucky fluke. Think about flipping a coin. If you flip it twice and get heads both times, is the coin rigged? Probably not. That's just luck. If you flip it 100 times and get 95 heads, then you’ve got something "significant" on your hands.
Technically, what is considered statistically significant is determined by a threshold called a p-value. Most scientists use a cutoff of 0.05. If your p-value is less than 0.05, you get to pop the champagne and call your findings significant. It means there is a less than 5% chance that you would see these results if there were actually nothing going on.
But here’s the kicker: 0.05 is completely arbitrary.
In the early 20th century, a guy named Ronald Fisher—a giant in the world of statistics—basically just picked 0.05 because it felt right. He thought one in twenty was a decent enough hurdle. Now, the entire global scientific community is obsessed with this one number. If a drug trial gets a p-value of 0.051, it’s often discarded as a failure. If it gets 0.049, it’s a breakthrough.
Does that seem a bit weird to you? It should.
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The P-Value Problem
The p-value doesn't measure how big an effect is. It doesn't tell you if a new business strategy will make you millions. It only tells you how confident we are that the result isn't a total accident.
Let’s say you’re testing a new weight loss pill. You run a massive study with 10,000 people. At the end, the group taking the pill lost an average of 0.2 pounds more than the group taking a sugar pill. Because the group was so big, the math might say this result is statistically significant.
But does it matter? No. Nobody is going to buy a pill to lose two-tenths of a pound. This is the difference between statistical significance and practical significance. Business owners fall into this trap all the time with A/B testing. They see a "significant" lift in clicks but ignore the fact that the actual revenue didn't budge.
Why context changes everything
If you’re testing a new flavor of soda, a p-value of 0.05 might be fine. If you’re testing the structural integrity of a nuclear reactor or a new heart surgery technique, 0.05 is terrifyingly high. In those cases, experts look for much lower numbers, like 0.001.
In physics, they use something called "Five Sigma." To claim they discovered the Higgs Boson particle, researchers at CERN needed a p-value of roughly $0.0000003$. That is a much higher bar than what your local marketing agency is using to tell you that "blue buttons perform better than red buttons."
The hidden trap of sample size
Size matters. A lot.
If your sample size is tiny, you need a massive effect to reach significance. If your sample size is huge, even tiny, irrelevant differences become significant. This is why you see so many "contradictory" health studies. One week eggs are killing you; the next week they’re a superfood. Often, these studies are just picking up noise because the groups they’re studying aren't controlled well enough, or the sample sizes are lopsided.
Real world examples of significance gone wrong
Look at the "replication crisis" in psychology. Famous studies that we all believed for decades—like the idea that "power posing" makes you more confident—have come under fire. Why? Because when other scientists tried to do the experiment again, the statistically significant results vanished.
The original researchers weren't necessarily lying. They might have just engaged in "p-hacking." This is when you run so many different types of analyses on your data that eventually, just by sheer luck, something clicks into that 0.05 range. If you test 20 different jelly bean colors to see if they cause acne, one of them will likely show a significant link just by random chance.
- P-hacking: Searching for patterns until you find one that fits the 0.05 criteria.
- Data dredging: Collecting a ton of data and looking for any correlation at all without a prior hypothesis.
- Publication bias: Journals usually only publish "significant" results. We never see the 99 studies that showed the "power pose" did nothing.
How to actually read the data
When someone shows you a chart, don't just look for the asterisk that says "p < 0.05." Ask about the Effect Size.
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The effect size tells you how much of a difference there actually is. If a new teaching method improves test scores by 20%, that’s a huge effect size. If it improves scores by 0.5%, even if it’s statistically significant, it’s probably not worth the cost of retraining all the teachers.
[Image comparing a small effect size vs a large effect size in two bar charts]
You also need to look at Confidence Intervals. Instead of giving you a single "yes or no" answer, a confidence interval gives you a range. It might say, "We are 95% sure that this new ad will increase sales by somewhere between 2% and 15%."
If that range includes zero? Then it's not significant. If the range is huge (like 0.1% to 50%), it means your data is messy and you probably shouldn't bet the company on it.
The move toward "Statistical Clarity"
Many experts are actually pushing to ban the phrase "statistically significant" altogether. In 2019, the American Statistical Association published a massive report arguing that the 0.05 threshold has caused more harm than good. They want researchers to describe their data in more nuanced ways instead of just treating it like a pass/fail exam.
For you, this means being a skeptic.
Next time you see a report at work, ask these three questions:
- How big was the sample size? (Too small is unreliable; too large makes tiny things look big).
- What is the actual "real world" difference? (The effect size).
- Has this been replicated, or is this the first time we've seen this result?
Actionable insights for decision makers
Stop chasing the p-value. If you want to use data to actually grow a business or make better health choices, follow these steps:
Prioritize Practicality Over Probability
Calculate the "Minimum Detectable Effect." Before you even start a test, decide how much of a change you actually need to see to make a move. If a 1% increase in conversion won't cover the cost of the change, then a 1% "significant" result is actually a failure.
Look at the Raw Data
Averages hide the truth. If you have one customer who spent $10,000 and 99 who spent $0, your "average" spend is $100. That is statistically significant compared to $0, but it doesn't represent your actual business reality. Look at the distribution, not just the mean.
Demand Replicated Results
Never make a major life or business change based on one study or one A/B test. Run it again. If the effect is real, it will show up a second time. If it was just a fluke of the statistically significant variety, it will likely disappear when you try to repeat the magic.
Embrace Uncertainty
The most honest answer in statistics is often "we aren't sure yet." If a result is borderline, don't force a "significant" label on it. It’s better to collect more data than to head in the wrong direction based on a shaky p-value.
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Data is a tool, not a crystal ball. Understanding that "significant" is just a math term—not a synonym for "important"—is the first step toward actually making smart, data-driven decisions.