Definition
A confidence interval is a range of values, computed from sample data, that is likely to contain the true population parameter. It provides both an estimate and a measure of uncertainty, telling you not just "what we think the answer is" but "how sure we are."
How It Works
A confidence interval has three parts: a point estimate (your best guess), a margin of error, and a confidence level (usually 95%).
A poll surveys 1,000 voters and finds 52% support a candidate.
The 95% confidence interval is 49% to 55%.
This means: we are reasonably confident the true level of support in the entire population falls between 49% and 55%. The race is too close to call because the interval includes 50%.
Why It Matters
Confidence intervals are more informative than p-values alone. While a p-value gives you a binary answer (significant or not), a confidence interval shows the range of plausible effect sizes. A narrow interval means high precision; a wide interval means more uncertainty.
You see confidence intervals in election polls (margin of error), medical research (treatment effects), and business analytics (conversion rate estimates). They help decision-makers understand not just what the data suggests, but how much trust to place in that suggestion.
A confidence interval gives you a range of plausible values, not just a single estimate. Wider intervals mean more uncertainty; narrower intervals mean more precision.