Confidence intervals, and why they say more than a p-value
A range of values your data are consistent with.
A 95% confidence interval is a range calculated so that, over many repetitions of the study, 95% of such intervals would contain the true value. In practice it is read as the range of effects your data are compatible with.
Why it matters
It carries the p-value's information and more. If a 95% interval for a difference in means excludes zero, the result is significant at p < 0.05 — but the interval also shows how large the effect might plausibly be, and how uncertain you are. A wide interval that just excludes zero is a much weaker finding than a narrow one far from it.
An example
'The mean difference in VAS pain was 1.3 points (95% CI 0.2 to 2.4).' Significant, since the interval excludes zero — but the data are consistent with anything from a trivial 0.2 to a substantial 2.4. That honesty is the point.
Compare: 'mean difference 1.3 (95% CI 1.1 to 1.5)' — the same estimate, far more precisely known.
Common mistakes
- Reading a confidence interval as 'a 95% chance the true value is in here'. The true value is fixed; it is the interval that varies.
- Reporting a p-value and no interval.
- Concluding two groups are the same because their intervals overlap. Overlapping intervals can still differ significantly.
Read next
- What a p-value is, and what it is not — The single most misreported number in postgraduate research.
- Effect size — how big, not just whether — Significance tells you something is there. Effect size tells you whether it matters.
Chavery Research Companion applies this to your own study: it asks the questions in plain language, checks the assumptions against your data, recommends the test, and writes the sentence that reports it. Start free — planning and the master chart cost nothing.