Calculate confidence intervals for a population mean (using t-distribution) or proportion (using z-distribution).
A confidence interval (CI) is a range of plausible values for an unknown population parameter (such as the true mean or proportion), based on your sample data.
A 95% CI means: if you repeated your study 100 times, approximately 95 of those intervals would contain the true population parameter. It does not mean there is a 95% probability that the true value is in this specific interval.
CI for a mean: Use when your outcome is a continuous measurement (height, weight, test score, reaction time). Uses the t-distribution and requires raw data.
CI for a proportion: Use when your outcome is a count of successes out of n trials — a proportion or rate (e.g. "45 out of 100 patients recovered"). Enter the number of successes (x) and total sample size (n).
A narrower CI is more informative. The width depends on:
Both carry similar information. The p-value tells you whether the effect is significant; the CI tells you both significance and the likely magnitude of the effect. Many journals now require CI reporting because it is more informative.
A key link: if a 95% CI for the difference between two means does not include 0, the corresponding two-sided p-value will be < 0.05.