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Confidence Intervals.

Build repeated confidence intervals and reveal which contain the generating mean.

Interactive experimentintuitiveField note ·
INTERACTIVE EXPERIMENT / M11

Confidence Intervals

Build repeated confidence intervals and reveal which contain the generating mean.

TEST THE MECHANISM
10100
-101

Horizontal scale: estimated mean. Each row is one interval; dots mark sample means.

Fixed true mean
Hidden
Intervals containing the mean
Hidden
Interval width
0.78

Each study’s exact Normal sample mean has variance 1/n, with known observation variance one. The procedure uses mean ± 1.96/√n. About 95% coverage concerns repeated intervals under the model, not a 95% probability assigned to this fixed parameter after observing one interval.

Coverage depends on independent Normal sampling and known variance. A fixed interval has no frequentist probability that its fixed parameter lies within it.

THE SHORT VERSION

Confidence Intervals, explained.

A frequentist confidence interval is produced by a procedure with a specified long-run coverage rate under its assumptions.

01 / THE MECHANISM

Why it happens

Sample means are Normal with true mean zero and known population standard deviation one. Each interval is mean ± 1.96 divided by the square root of sample size; forty intervals show realised coverage.

A frequentist confidence interval is produced by a procedure with a specified long-run coverage rate under its assumptions.

Read the result

Count intervals containing zero after the reveal. Coverage is a property of the repeated procedure, not a posterior probability for one fixed interval.

02 / FOLLOW IT THROUGH

A worked example

Forty studies of a fixed mean

  1. Generate forty independent Normal sample means with the selected sample size and known variance.

  2. Reveal the fixed generating mean of zero and inspect which intervals cross its vertical reference line.

  3. Increase sample size to narrow intervals while keeping the same nominal long-run coverage rule.

OPTIONAL DEEPER DETAILGo deeper: inside the model

Inside this model

Sample means are Normal with true mean zero and known population standard deviation one. Each interval is mean ± 1.96 divided by the square root of sample size; forty intervals show realised coverage.

03 / BEYOND THE EXPERIMENT

Where this idea is useful

A practical use

Report the estimate and its uncertainty together, and ask which sampling and model assumptions support the stated coverage.

CHECK YOUR INTUITION

A common misconception

THE TEMPTING CONCLUSION

“A 95% interval contains 95% of observations.”

THE MORE USEFUL DISTINCTION

The interval estimates a population parameter. It is not a prediction interval for individual observations or a description of their central spread.

What this explanation leaves out

  • Coverage depends on independent Normal sampling and known variance. A fixed interval has no frequentist probability that its fixed parameter lies within it.
ONE MORE QUESTION

Must exactly thirty-eight intervals contain zero?

No. Ninety-five percent is a long-run procedure rate under the assumptions; a batch of forty can have a different realised count.

TAKE THE IDEA WITH YOU

Which parameter, sampling process and interval procedure justify your uncertainty statement?

Associated thinkers

Associated withJerzy Neyman ↗

Associations marked provisional are awaiting source review.

Further reading

Explore the original research or the teaching reference behind this experiment.