A p-value is a statistical measurement used to determine whether the results of a study are likely due to chance or reflect a real effect. It helps researchers decide whether to accept or reject the null hypothesis, which is the assumption that there is no difference or relationship between the variables being studied.
In simple terms, a low p-value suggests that the observed results are unlikely to have occurred by random chance, and are therefore more likely to reflect a real association or effect.
Why Are They Called “P-Values”?
The “p” in p-value stands for probability. Specifically, it refers to the probability of observing a result as extreme as, or more extreme than, the one seen in the study—assuming the null hypothesis is true. In other words, a p-value tells you how likely your results would be just by random chance if there were actually no real effect.
P-values range from 0 to 1:
A p-value of 1.0 means the observed results are completely consistent with random chance.
A p-value of 0.05 or lower (often the chosen cutoff for significance) means there's less than a 5% probability that the results occurred by chance.
The lower the p-value, the stronger the evidence against the null hypothesis.
💡 Example: A p-value of 0.025 means there’s a 2.5% chance that the result is due to random variation rather than a true difference.
If a p-value is below the threshold (usually 0.05), the result is considered statistically significant. This suggests that the finding is unlikely to be random and may warrant further investigation or influence medical decisions.
However, statistical significance does not mean a result is:
Clinically important (a tiny change might be significant but not meaningful to patients)
Free of bias
Proven to be causal
Imagine a study investigating survival outcomes after surgery in patients with a rare cancer. Researchers look at tumor size, cell type, and treatment combinations. Here are their findings:
Variable Studied | Finding | P-Value | Interpretation |
|---|---|---|---|
Tumor size < 4 cm vs ≥ 4 cm | Larger tumors had worse survival | 0.025 | Statistically significant; size likely influences outcome |
Epithelioid vs. Biphasic histology | Biphasic tumors had shorter survival (1.5 years median) | 0.039 | Statistically significant; histology affects prognosis |
Surgery alone vs. surgery + chemotherapy | No meaningful difference in survival | 0.59 | Not significant; may reflect small sample size or true equivalence |
Age < 65 vs ≥ 65 | Older patients had worse outcomes | 0.08 | Not statistically significant, but may suggest a trend |
A p-value is not the probability the hypothesis is true. It only measures how likely the observed data would be if the null hypothesis were true.
A p-value > 0.05 does not mean there is “no effect.” It means the evidence isn’t strong enough to rule out chance.
Statistical significance ≠ clinical importance. A finding can be statistically significant but still not matter much in practice.
P-values are used in nearly all peer-reviewed medical studies to:
Test the effectiveness of treatments or interventions
Compare outcomes between patient groups
Evaluate risk factors and prognostic indicators
Understanding how to interpret p-values helps patients and professionals make more informed decisions based on research evidence.
