# What are confidence intervals and p-values?

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Author: **Paul Bassett**

A confidence interval calculated for **a measure of treatment effect **shows the range within which the true treatment effect is likely to lie.

A p-value is calculated to assess whether differences between treatments are likely to have occurred simply through chance, or whether they are likely to represent a genuine effect.

Confidence intervals are preferable to p-values, as they tell us the **range of possible effect sizes **compatible with the data, and thus provide clinically relevant information.

P-values simply provide a cut-off beyond which we assert that the findings are ‘statistically significant’ (by convention, this is p<0.05).

A confidence interval that **embraces the value of no difference between treatments** indicates that the treatments are not significantly different.

Confidence intervals **aid interpretation **by putting upper and lower bounds on the likely size of any true effect.

**Non-significance does not mean ‘no effect’**. Small studies will often report non-significance even when there are important, real effects which a large study would have detected.

Statistical significance does not necessarily mean that the effect is real: by chance alone about one in **20 significant findings will be spurious.**

Statistically significant does not necessarily mean clinically important.

It is the **size of the effect **that determines the clinical importance, not the presence of statistical significance.

While confidence intervals may be preferable to p-values, the latter provide complementary information, and both can be reported together.