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A small p-value tells you a difference is probably real, not that it's big enough to matter. Statistical significance asks whether an effect is distinguishable from noise; biological significance asks whether it's large enough to change a cell, a patient, or a conclusion. Because the p-value shrinks as your sample grows, a big enough study can stamp p < 0.05 on a difference that means nothing. Here's why, with real examples, and what to report instead: effect size and confide

'P < 0.05'? This single number dictates whether a new drug is 'effective' or a finding is 'significant,' but what does it actually mean? P-values are perhaps the most misunderstood concept in biomedical data analysis. This article strips away the jargon. We'll explain exactly what a p-value is (hint: it's a 'measure of surprise'), how to interpret that 0.05 threshold, and the crucial, often-missed difference between 'statistical significance' and 'clinical significance.'