Statistical significance is a way of measuring whether a result from a study or experiment is likely real or just due to random chance. When researchers call something “statistically significant,” they’re saying the pattern they found is unlikely to have appeared if nothing meaningful were actually going on. The standard threshold is a less than 5% probability that the result happened by luck alone.
How Statistical Significance Works
The core idea starts with an assumption called the null hypothesis. This is essentially the boring explanation: that there’s no real difference or effect. If you’re testing whether a new medication works better than a placebo, the null hypothesis says “it doesn’t.” Researchers then collect data and run calculations to see how well that boring explanation holds up.
The result of those calculations is a p-value, a number between 0 and 1. A p-value tells you the probability of seeing results at least as extreme as what you got, assuming the null hypothesis is true. A p-value of 0.03, for example, means there’s only a 3% chance you’d see data this dramatic if nothing real were happening. The smaller the p-value, the harder it is to blame the result on chance.
Before running the experiment, researchers pick a cutoff called the alpha level. If the p-value comes in below that cutoff, the result is declared statistically significant. Almost universally, that cutoff is set at 0.05, or 5%. When you see a study reporting “p < 0.05,” it means the researchers found less than a 5% probability that their results are just noise.
Why 0.05 Is the Standard (and Why It’s Arbitrary)
The 0.05 threshold traces back to Ronald Fisher, one of the founders of modern statistics. Fisher argued that a 1-in-20 chance of being wrong was a convenient limit for judging whether a result should be taken seriously. That’s it. There’s no deep mathematical proof that 0.05 is the right number. It was a practical judgment call that became tradition, and the research world has been using it ever since.
This matters because the line between “significant” and “not significant” is thinner than most people realize. A p-value of 0.049 gets labeled significant. A p-value of 0.051 does not. Yet those two numbers reflect nearly identical evidence. Some researchers have argued for stricter thresholds like 0.005, while others point out that results hovering near 0.05 may still be clinically relevant, especially in smaller studies. The cutoff is a tool, not a truth.
What a P-Value Does Not Tell You
This is where most people get tripped up. A p-value of 0.03 does not mean there’s a 3% chance the treatment doesn’t work. It also doesn’t mean there’s a 97% chance it does work. The American Statistical Association released a formal statement clarifying this: p-values do not measure the probability that a hypothesis is true or false. They only describe how compatible the data are with the assumption that nothing is happening.
A p-value also says nothing about how big or important an effect is. It only tells you whether the effect is likely real. Two studies can both report p = 0.01 while describing completely different levels of impact. One cancer drug might extend survival by five years, while another extends it by five months, both with the same p-value. The statistical test treats them equally, even though the real-world meaning is vastly different.
Statistical Significance vs. Practical Significance
This distinction matters most in health and medicine. A result can be statistically significant but practically meaningless. The reason is sample size. Very large studies tend to transform tiny differences into statistically significant ones, even when those differences are too small to matter to anyone. If you run a study with thousands of patients, you might detect a blood pressure drop of 0.5 points with a p-value well below 0.05. That’s a real difference in the statistical sense, but no patient would notice it and no doctor would change a treatment plan over it.
The reverse is also true. A small study might test a genuinely effective treatment but fail to reach statistical significance simply because there weren’t enough participants to detect the effect reliably. This is why researchers increasingly emphasize reporting the effect size alongside the p-value. Effect size measures the magnitude of a difference, and unlike p-values, it doesn’t change based on how many people were in the study.
Confidence Intervals: A More Complete Picture
One of the biggest criticisms of p-values is that they force a binary yes-or-no decision. Either the result is significant or it isn’t. Confidence intervals offer more nuance. A 95% confidence interval gives you a range of values that likely contains the true effect. If a study finds that a treatment lowers cholesterol by 10 points with a 95% confidence interval of 3 to 17, you know the effect is probably somewhere in that range, and because the entire range is above zero, you also know it’s statistically significant.
Confidence intervals tell you three things at once: whether the result is significant, how large the effect might be, and how precise the estimate is. A narrow interval means the study pinpointed the effect well. A wide interval means there’s a lot of uncertainty. This is far more useful than a single p-value, which strips all of that context away.
P-Hacking: How Significance Gets Manipulated
Because careers and publications often depend on producing statistically significant results, some researchers (intentionally or not) engage in practices that inflate their chances of hitting that 0.05 threshold. This is known as p-hacking, and it’s a well-documented problem across scientific fields.
Common forms of p-hacking include checking results partway through an experiment and stopping early if the numbers look good, measuring many different outcomes and only reporting the ones that came back significant, removing outliers after seeing how they affect the results, and trying multiple statistical tests until one produces p < 0.05. Each of these practices inflates the false positive rate well beyond the intended 5%. A study that looks rigorous on paper may have arrived at its “significant” result through a process that was anything but.
The American Statistical Association’s guidelines are blunt on this point: all analyses should be reported fully, and a low p-value should never be the sole basis for a scientific claim. Proper interpretation requires knowing how the study was designed, how the data were collected, and whether the analysis plan was set before the researchers looked at the results.
How to Read “Stat Sig” Results
When you encounter a claim that something is statistically significant, three questions will help you gauge whether it actually matters. First, how large was the effect? A significant p-value with a tiny effect size is often unimportant. Second, how big was the study? Very large sample sizes can make trivial differences look significant. Third, was this the only analysis, or were multiple comparisons tested? The more tests you run, the more likely you are to get a false positive by chance alone.
Statistical significance is a useful filter for separating signal from noise, but it was never meant to be the final word. It tells you that a result probably isn’t random. It doesn’t tell you that a result is large, important, or relevant to your life. The p-value is the starting point of interpretation, not the end of it.