Compute confidence intervals for your sample data to quantify uncertainty in your estimates.
Sample Mean
Standard Deviation
Sample Size (n)
Confidence Level (90, 95, or 99)
01
Choose the statistic or interval type supported by the calculator, such as mean or proportion.
02
Enter the sample values requested by the page, including sample size and variability inputs.
03
Select the confidence level, such as 90%, 95%, or 99%.
04
Review the lower and upper bounds and interpret the interval alongside the sample context.
01
Turns sample data into a clear uncertainty range instead of a single-point estimate.
02
Helps analysts communicate precision around means, proportions, and experiment results.
03
Shows how sample size, variability, and confidence level affect statistical certainty.
04
Supports better decisions by making uncertainty visible before conclusions are drawn.
A confidence interval is a range of plausible values for a population estimate. It shows the uncertainty around a sample statistic such as a mean or proportion.
A 95% confidence interval means that the method would capture the true population value in about 95 out of 100 repeated samples, assuming the model assumptions hold.
Common inputs include sample mean or proportion, sample size, standard deviation or standard error, and the chosen confidence level.
For a two-sided 95% confidence interval under a normal approximation, the commonly used z-score is about 1.96.
Smaller sample size, higher variability, and a higher confidence level can all make the interval wider.
No. A confidence interval estimates uncertainty around a population parameter, while a prediction interval estimates where a future individual observation may fall.
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