Confidence Interval for the Mean Calculator
Enter the sample mean x̄, sample standard deviation s, sample size n and confidence level (e.g. 95 %) to get a two-sided confidence interval for the population mean μ — including the critical value (t* or z*), the standard error SE = s/√n, the margin of error ME, the degrees of freedom and the final [lower, upper] interval.
Enter valid values: x̄ finite, s > 0, n an integer ≥ 2, confidence between 0–100 %.
95% confidence interval for population mean μ
[46.70, 53.30]
CI = 50 ± 3.30
Standard error (SE)
—
SE = s ÷ √n
t*
—
Two-sided critical value (t* or z*).
Degrees of freedom
—
df = n − 1 (used only with t).
Interpretation: if you drew many samples and built an interval each time, ~95 % (or your chosen level) would cover the true mean μ. It is NOT a 95 % probability that μ is inside this particular interval.
Formula
CI = x̄ ± t*·(s / √n), df = n − 1 z-distribution variant (population σ known): CI = x̄ ± z*·(σ / √n) t* and z* are the two-sided critical values for the chosen confidence level (e.g. 95 % → t*_{df, 0.975}).
- · The t distribution is the default because the sample SD almost never equals the true population σ. For large samples (n ≥ 30) the t* and z* values converge — but using t is always defensible and matches modern textbooks.
- · Switch to z only when you genuinely know the population σ (e.g. a manufacturing spec or an instrument calibration). Intro stats courses use z for clarity, but real data analyses prefer t.
- · Critical values t* come from the inverse CDF of Student's t (df = n − 1); z* comes from the inverse standard-normal CDF. This tool computes the regularised incomplete beta function via the Numerical Recipes continued-fraction expansion, then inverts with Newton–Raphson.
- · The 95 % level (α = 0.05, t*_{df, 0.975}) is the standard. Common alternatives are 90 % (α = 0.10) and 99 % (α = 0.01). Higher confidence → wider interval; larger n → narrower interval (proportional to 1/√n).
- · Assumptions: simple random sample and approximately normal data (or large n via the Central Limit Theorem). For strongly skewed data or obvious outliers, consider bootstrap intervals or rank-based methods (e.g. Wilcoxon).
- · Common misconception: "there is a 95 % probability that μ lies in this interval" is WRONG — μ is a fixed constant. The correct interpretation is frequentist: in repeated sampling, ~95 % of the intervals constructed this way would cover μ.
Frequently asked
Should I use the t distribution or the z distribution?
Default to t. In practice you only have the sample standard deviation s, not the true population σ — and s itself carries sampling error, which the wider t distribution accounts for. Use z only when σ is genuinely known (rare: think calibrated instrument specs or long-established physical constants). For n ≥ 30 the two are nearly identical; at n = 10, t*_{9, 0.975} ≈ 2.262 is already ~15 % wider than z*_{0.975} ≈ 1.960.
What does "95 % confidence" actually mean?
The correct frequentist reading is: "if I repeat this sampling procedure many times and construct an interval each time, ~95 % of those intervals will cover the true mean μ." It is NOT correct to say "there is a 95 % probability that μ is inside this particular interval" — μ is a fixed constant; it is either in or out. The 95 % describes the long-run reliability of the procedure, not this single interval. If you want a probability statement about μ directly, you need a Bayesian "credible interval" instead.
How much do I need to increase n to halve the margin of error?
About four times the sample. Since ME = t*·s/√n, and t*, s are roughly fixed, ME scales as 1/√n. To halve ME, √n must double, so n must quadruple. To cut ME to one-third you need 9× the sample. This is the classic diminishing returns of statistics — going from 100 samples to 400 doubles your precision, and to 1 600 doubles it again. In practice, improving sample *quality* (reducing measurement error, avoiding biased sampling) often pays off more than blindly increasing n.
Can I still use this with a tiny sample like n = 2 or 3?
Mathematically yes (df = n − 1 ≥ 1, the t distribution is well-defined), but the interval will be enormous: t*_{1, 0.975} ≈ 12.7 for n = 2 and ≈ 4.30 for n = 3. Even an apparently precise sample mean can be miles away from the true μ. The normality assumption also becomes critical at small n — and you cannot verify it with two or three points. A practical rule is n ≥ 10 to even attempt this, n ≥ 30 for it to behave nicely. With only a handful of observations consider bootstrap resampling or Bayesian alternatives instead.
How is a confidence interval related to a t-test?
They are two sides of the same coin. Using a 95 % CI to test the hypothesis "μ = μ₀": μ₀ falls outside the interval ⟺ a two-sided t-test rejects H₀ at α = 0.05. The confidence interval is essentially the set of all μ₀ values that the t-test would not reject. This is why many statisticians prefer reporting confidence intervals over bare p-values — the interval shows both the effect size and the precision, while a p-value only answers "significant or not".
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