Lesson 7 of 8 · All lessons in this module
  1. Populations, samples and sampling error
  2. Stratified and cluster sampling
  3. Convenience and judgmental sampling, and choosing a method
  4. Sample size and mixing populations
  5. The central limit theorem
  6. The standard error of the sample meanVideo lesson
  7. Confidence intervals for the mean
  8. Bootstrap and jackknife resampling

Lesson 7 of 8 · 15 min

Confidence intervals for the mean

A confidence interval turns a point estimate into a range: point estimate ± reliability factor × standard error.

In short

  • A point estimate (e.g. Xˉ\bar X) is a single best guess of a parameter. An interval estimate, or confidence interval, is a range built around it with a stated confidence level 1−α1 - \alpha.
  • Structure: point estimate ± reliability factor × standard error. The reliability factor is the critical value (z or t) that leaves α/2\alpha/2 in each tail.
  • z-values to know: 1.645 (90%), 1.96 (95%), 2.576 (99%). A value with 0.025 in one tail belongs to a 95% interval; 0.05 in one tail belongs to a 90% interval.
  • Use z when σ\sigma is known (normal population, or any population with a large sample). Use t with n − 1 degrees of freedom when σ\sigma is unknown. With a small sample from a clearly non-normal population, neither z nor t gives a valid interval.
  • The interval widens with a higher confidence level, a larger standard deviation, or a t instead of a z. It narrows as n grows, in proportion to 1/n1/\sqrt{n}.

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