Confidence Interval
Statistics & analysis
A range around a sample estimate within which the true population value plausibly lies — a 95% CI conveys both the estimate and its precision.
A confidence interval is a range of plausible values for a population parameter, calculated from sample data. A 95% confidence interval means that if the study were repeated many times, roughly 95% of the intervals constructed this way would contain the true population value.
The width of the interval communicates precision. A narrow interval indicates a well-estimated effect; a wide one indicates considerable uncertainty. Width shrinks as sample size grows and as variability falls, which is why an interval conveys more information than a p-value on its own.
There is a direct link to significance testing: a 95% interval for a mean difference that excludes zero corresponds to a significant result at p below 0.05. Journals increasingly require intervals alongside effect sizes precisely because together they show magnitude and uncertainty, rather than reducing a finding to significant or not.
Where it's used
- Reporting effects with their uncertainty, as journals increasingly require
- Interpreting whether an effect is precise enough to act on
Software used
Guides that use this concept
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