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About

A confidence interval defines the range within which a population parameter likely exists. Researchers use it to express the reliability of an estimate. A narrow interval suggests high precision while a wide interval indicates significant uncertainty. The calculation method depends on the sample size and whether the population standard deviation is known. For large samples we use the Z-distribution. For smaller samples under thirty we rely on the T-distribution which has heavier tails to account for added uncertainty.

statistics confidence interval z-score t-test inference

Formulas

The general structure for the interval is point estimate plus or minus the margin of error.

CI = x ± Z sn

Where sn is the Standard Error.

Reference Data

Confidence LevelZ-Score ( df)T-Score (df=10)Alpha (α)
80%1.2821.3720.20
85%1.4401.5590.15
90%1.6451.8120.10
95%1.9602.2280.05
98%2.3262.7640.02
99%2.5763.1690.01
99.5%2.8073.5810.005
99.9%3.2914.5870.001

Frequently Asked Questions

It means that if you repeated the experiment or sampling infinite times, 95% of the calculated intervals would contain the true population parameter. It does not mean there is a 95% chance the parameter is in this specific interval.
Use the T-score when the sample size is small (generally n < 30) and the population standard deviation is unknown. As the sample size grows, the T-distribution converges into the Z-distribution.
You can narrow the interval by increasing the sample size or by accepting a lower confidence level (e.g., dropping from 99% to 95%). A smaller standard deviation in the data also narrows the interval.