By convention, a result with p < 0.05 is called statistically significant. That means that, if there were no real effect, such a result would appear by chance less than once in twenty times. It does not say the effect is large or important, nor that it is true with 95% probability.
Two misinterpretations are very common. The first is confusing statistical significance with practical relevance: with huge samples, tiny differences are significant. The second is thinking a “non-significant” result proves there is no effect: with small samples, real effects go unnoticed.
That is why good studies also report the effect size and its confidence interval, which show how much something changes and how precisely it has been measured.
In small studies with many measurements, some results will come out significant by chance alone. Several isolated “significant” tests do not add up to a solid finding.
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