Study Tips
How to Understand Statistical Significance
By Dr Ayesha Khan · · 4 min read
Quick answer
Statistical significance indicates that a result is unlikely to have arisen by chance alone, given a threshold you set in advance. It does not mean the result is large, important, or certainly true. A p-value below the threshold suggests the effect is probably real, but significance and importance are different things.
What significance means
A statistically significant result is one unlikely to have occurred by chance, if there were really no effect. It gives you some confidence that a pattern in your data reflects something real rather than random variation.
The core idea is ruling out chance as the explanation. Significance is about how surprising the result would be if nothing were actually going on.
The p-value
The p-value is the probability of getting a result at least as extreme as yours if there were no real effect. A small p-value means such a result would be unlikely by chance, which counts as evidence against 'no effect'.
The threshold — often set at a conventional level — is decided in advance. Below it, the result is called significant; above it, it is not.
What it does not mean
Significance does not mean the effect is large or important. A tiny, trivial effect can be statistically significant with a big enough sample. Significance is about confidence that an effect exists, not its size.
This is the most important and most-missed point. A significant result can be practically meaningless, and an important effect can fail to reach significance in a small study.
Significance versus importance
Because of this, always ask two separate questions: is the effect real (significance), and is it big enough to matter (effect size)? Both are needed to interpret a result properly.
Reporting only significance, without the size of the effect, tells half the story. Good interpretation considers both.
Sample size matters
Larger samples make it easier to detect small effects as significant, because they reduce the role of chance. This is why very large studies can find significance in effects too small to care about.
Understanding this stops you over-interpreting significance in huge datasets, or dismissing a promising effect that a small study could not confirm.
Interpreting honestly
Statistical significance is a tool, not a verdict. It informs judgement rather than replacing it, and it is easily misused — treating the threshold as a magic line, or significance as proof.
The honest reading is cautious: a significant result is evidence worth taking seriously, considered alongside the effect size, the study design, and other findings.
Frequently asked questions
What does statistically significant mean?+
That a result is unlikely to have arisen by chance alone, if there were really no effect. It gives some confidence that a pattern reflects something real rather than random variation.
What is a p-value?+
The probability of getting a result at least as extreme as yours if there were no real effect. A small p-value means such a result would be unlikely by chance, counting as evidence against 'no effect'.
Does significant mean the effect is important?+
No — this is the most-missed point. A tiny, trivial effect can be statistically significant with a large enough sample. Significance is about confidence that an effect exists, not its size or importance.
Why does sample size matter?+
Larger samples make it easier to detect small effects as significant, because they reduce the role of chance. This is why very large studies can find significance in effects too small to matter.
How should I interpret a significant result?+
Cautiously, alongside the effect size and study design. Significance is evidence worth taking seriously, not proof, and always ask separately whether the effect is both real and big enough to matter.
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