Enter Values

Average value from your sample
Enter as decimal (0.52 = 52%)
Known population standard deviation
Sample standard deviation
Number of observations (must be whole number)
Confidence Interval Formulas
CI = x̄ ± t* × (s / √n)
= Sample mean | t* = Critical value | s = Sample std dev | n = Sample size

Confidence Interval

95% Confidence Interval [93.81, 106.19] t-interval
Point Estimate 100.00
Standard Error 3.00
Critical Value 2.0639
Margin of Error 6.19

Formula Breakdown

CI = x̄ ± t*n-1 × (s / √n)
t* is the upper (1 - α/2) quantile of the t-distribution

Interpretation Guide

Correct interpretation: If we repeated this sampling process many times, approximately 95% of the resulting confidence intervals would contain the true population mean.

Note: This does not mean there is a 95% probability the true value lies in this specific interval.

Interval Type Use When Critical Value
z-interval σ is known z* from N(0,1)
t-interval σ unknown, use s t* from t(n-1)
Proportion (Wald) np̂ ≥ 5, n(1-p̂) ≥ 5 z* from N(0,1)
Model Assumptions
  • Sample is a simple random sample from the population
  • Observations are independent
  • For t-interval: population is approximately normal, or n is large (CLT)

For educational purposes. Not financial advice. Market conventions simplified.

Ryan O'Connell, CFA
CALCULATOR BY
Ryan O'Connell, CFA
CFA Charterholder & Finance Educator

Finance professional building free tools for options pricing, valuation, and portfolio management.

Understanding Confidence Intervals

What is a Confidence Interval?

A confidence interval is a range of values constructed from sample data that is designed to contain the true population parameter with a specified probability over repeated sampling. Unlike a point estimate (single value), a confidence interval quantifies the uncertainty in our estimate.

General Confidence Interval Formula
CI = Point Estimate ± (Critical Value × Standard Error)
The margin of error determines interval width

When to Use Each Interval Type

Z-Interval

Known σ
Use when population standard deviation is truly known (rare in practice). Formula: x̄ ± z* × (σ/√n)

T-Interval

Unknown σ
Use when estimating σ from sample. Accounts for extra uncertainty with wider intervals. Formula: x̄ ± t* × (s/√n)

Common Misinterpretation

Incorrect: "There is a 95% probability the true value lies in this interval."

Correct: "If we repeated this sampling process many times, approximately 95% of the resulting confidence intervals would contain the true population parameter."

The true parameter is fixed (not random). The randomness comes from the sampling process, not the parameter itself.

Key Assumptions

  • Random sampling: Sample must be randomly selected from the population
  • Independence: Observations must be independent of each other
  • For t-interval: Population approximately normal OR large sample (n ≥ 30 by rule of thumb)
  • For proportion interval: np̂ ≥ 5 and n(1-p̂) ≥ 5 for normal approximation validity
Wald Interval Limitation: The Wald (normal approximation) proportion interval can produce bounds outside [0, 1] when p̂ is near 0 or 1. For better coverage near boundaries, consider the Wilson score or Agresti-Coull intervals.
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Frequently Asked Questions

A confidence interval is a range of values constructed from sample data that is designed to contain the true population parameter with a specified probability over repeated sampling. The confidence level (e.g., 95%) indicates the long-run proportion of such intervals that would contain the parameter if the sampling procedure were repeated many times.

Use a z-interval when the population standard deviation (σ) is known, which is rare in practice. Use a t-interval when σ is unknown and you estimate it with the sample standard deviation (s). The t-interval accounts for the additional uncertainty from estimating σ, producing wider intervals for smaller samples.

A 95% confidence level means that if we repeated the sampling process many times and constructed a confidence interval each time, approximately 95% of those intervals would contain the true population parameter. It does NOT mean there is a 95% probability the true value is in any specific realized interval - that would be a Bayesian interpretation of a fixed but unknown parameter.

The t-distribution has heavier tails because it accounts for the uncertainty introduced by estimating the population standard deviation from the sample. With smaller samples, this estimation is less precise, requiring wider intervals (larger critical values) to maintain the stated confidence level. As degrees of freedom increase, the t-distribution approaches the standard normal.

With small samples (n less than 30), the t-interval requires the population to be approximately normally distributed for the stated coverage to hold. If the population is heavily skewed or has extreme outliers, the t-interval may not achieve the nominal coverage probability. Consider nonparametric methods or bootstrap confidence intervals for non-normal populations with small samples.

A confidence interval for a proportion estimates the true population proportion. This calculator uses the Wald (normal approximation) interval. For example, a 95% CI of [0.49, 0.55] for a sample proportion of 0.52 was produced by a procedure that captures the true proportion in about 95% of repeated samples. The Wald interval requires sufficient successes and failures (both at least 5) for the normal approximation to be valid.
Disclaimer

This calculator is for educational purposes only. It uses standard statistical formulas and assumptions. The t-interval assumes approximately normal populations for small samples. The Wald proportion interval uses normal approximation and may produce bounds outside [0, 1] for extreme proportions. For critical applications, verify results with statistical software and consider alternative interval methods. This tool should not be used as the sole basis for important decisions.