P(Lθ < θ < Uθ) = 1 − α
Lθ and Uθ are random variables that change from sample to sample, so this is a genuine probability statement.
✔ Legitimate probability statement
Point vs. interval estimates, what "95% confident" really means, and how confidence trades off against precision.
A point estimate is a single value (x̄ for μ). An interval estimate gives a lower and upper value, a confidence interval, and is usually more useful.
x̄ = 68.4 · 95% CI [63.5, 73.3]
Hidden truth: μ = 65, σ/√n = 2.5. Draw several samples: the single guess almost never hits exactly; the net usually catches μ.
Repeated samples from a population with μ = 60, σ = 10, n = 16. In repeated sampling, about 100(1 − α)% of intervals built this way contain the parameter.
Press “Draw 20 new samples”
Switching the level re-scores the same samples with wider or narrower nets. The more you draw, the closer the cumulative rate gets to 95%.
The most common CI misconception: mixing up the interval before the sample is drawn with the interval after.
P(Lθ < θ < Uθ) = 1 − α
Lθ and Uθ are random variables that change from sample to sample, so this is a genuine probability statement.
✔ Legitimate probability statement
C(60 < μ < 80) = 95%
Once numbers are plugged in, nothing is random. The interval either contains θ or it doesn't, so we say confident, not “probable”.
⚠ A confidence statement, not a probability
You computed a 95% interval of [52.4, 67.6]. Is there a 95% probability that μ lies between 52.4 and 67.6?
anchored at x̄ = 60, SE = 2.5
↑ More confidence: fewer missed parameters, but a less precise estimate.
↓ Narrower net: sharper estimate, but the risk of missing μ goes up.
§1.4 Measurement Scales →