Is 99% better than 95%?

A 99% interval is wider: more confident but less precise. 95% is the convention.

What if the interval includes zero?

You can't rule out no effect, so the result isn't statistically significant.

Confidence interval

➗ Mathematics & Statistics By CampusDown Wiki Editorial Team Last updated October 26, 2026

A confidence interval expresses the uncertainty in a sample estimate, such as a mean or proportion, as a range (estimate ± margin) likely to contain the true population value.

Contents

1. History
2. Calculation and correct interpretation
3. In practice
4. FAQ
5. References

History

Polish-born statistician Jerzy Neyman formalized confidence intervals in a 1937 paper. Ronald Fisher pushed a rival "fiducial" approach, and the two clashed bitterly, but Neyman's frequentist interpretation became the standard. From the 1980s, medical journals began requiring CIs instead of bare p-values. After the replication crisis of the 2010s, psychology adopted "the new statistics," which centers reporting on effect sizes and intervals.

Calculation and correct interpretation

A 95% CI for a mean is roughly the sample mean ± 1.96 × standard error. The standard error is the standard deviation divided by √n, so quadrupling the sample halves the width. The 1.96 cuts off 2.5% in each tail of the normal distribution; small samples use a t value instead. A common misreading is "there's a 95% chance the true value is in this interval." Strictly, if you repeated the sampling 100 times, about 95 of the intervals would contain the true value. The 95% describes the method, not any single interval.

In practice

A poll's "±3.1 points at 95% confidence" is a confidence interval. If two candidates are within the margin, you can't call the race. If a CI for a difference excludes zero, the result is significant at the 5% level, the same verdict a t-test gives. The CI also shows the effect's size and precision. To get the precision you want, calculate the sample size in advance.

FAQ

Is 99% better than 95%?: A 99% interval is wider: more confident but less precise. 95% is the convention.
What if the interval includes zero?: You can't rule out no effect, so the result isn't statistically significant.

Sources reviewed: 2026-10-26

References

Neyman, J. (1937). Outline of a theory of statistical estimation based on the classical theory of probability. Phil. Trans. R. Soc. A, 236, 333–380.
Cumming, G. (2014). The new statistics: Why and how. Psychological Science, 25(1), 7–29.
Greenland, S., et al. (2016). Statistical tests, P values, confidence intervals, and power. Eur J Epidemiol, 31, 337–350.

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This article was last updated on October 26, 2026. It is based on widely recognized original works and textbooks; when citing it in a paper, please check the original sources listed in the references.