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Confidence Interval Calculator – Find CI for Mean Online

Calculate confidence intervals for population means with our free online confidence interval calculator. Supports 90%, 95%, and 99% confidence levels with margin of error shown.

Examples:

Understanding Confidence Intervals

A confidence interval gives you a range of plausible values for a population parameter – like the true average – based on your sample data. Instead of saying "the average height is 170 cm," you say "we're 95% confident the true average is between 168 and 172 cm." That range is the confidence interval.

The confidence level (90%, 95%, 99%) tells you how certain you want to be. Higher confidence means a wider interval – you cast a bigger net to be more sure you've caught the true value. A 99% confidence interval is wider than a 95% interval for the same data because you're demanding more certainty.

Here's what "95% confident" really means: if you took 100 different samples and calculated 100 confidence intervals, about 95 of them would contain the true population mean. Any single interval either contains the true mean or it doesn't, but the method works 95% of the time in the long run.

The Confidence Interval Formula

CI = x̄ ± Z × (σ / √n)
Sample mean (your best estimate)
Z
Z-score for your confidence level
σ
Population standard deviation
n
Sample size
σ/√n
Standard error of the mean
Z × (σ/√n)
Margin of error

Common Z-Scores

90% ConfidenceZ = 1.645
95% ConfidenceZ = 1.96
98% ConfidenceZ = 2.326
99% ConfidenceZ = 2.576
99.9% ConfidenceZ = 3.291

What Affects Interval Width

Sample Size (n)
Larger samples give narrower intervals (more precision)
Standard Deviation (σ)
More variability means wider intervals
Confidence Level
Higher confidence = wider interval

Worked Examples

Example 1: Test Scores

A sample of 100 students has mean score 75 with standard deviation 10. Find the 95% confidence interval.

x̄ = 75
σ = 10
n = 100
Z (95%) = 1.96
Standard Error = σ/√n = 10/√100 = 10/10 = 1
Margin of Error = Z × SE = 1.96 × 1 = 1.96
CI = 75 ± 1.96 = (73.04, 76.96)
We're 95% confident the true population mean test score is between 73.04 and 76.96.

Example 2: Adult Heights

Sample of 50 adults has mean height 170 cm with standard deviation 8 cm. Calculate 95% CI.

x̄ = 170 cm
σ = 8 cm
n = 50
Z (95%) = 1.96
Standard Error = 8/√50 = 8/7.07 = 1.13
Margin of Error = 1.96 × 1.13 = 2.22
CI = 170 ± 2.22 = (167.78, 172.22) cm
The true mean height is likely between 167.78 and 172.22 cm with 95% confidence.

Example 3: Household Income

Survey of 200 households finds mean income $52,000 with standard deviation $15,000. Find 99% CI.

x̄ = $52,000
σ = $15,000
n = 200
Z (99%) = 2.576
Standard Error = 15000/√200 = 15000/14.14 = 1060.66
Margin of Error = 2.576 × 1060.66 = 2732.26
CI = $52,000 ± $2,732 = ($49,268, $54,732)
With 99% confidence, the true mean household income is between $49,268 and $54,732.

Example 4: Product Satisfaction

150 customers rate a product 4.2 out of 5 (σ = 0.8). Find the 95% confidence interval.

x̄ = 4.2
σ = 0.8
n = 150
Z (95%) = 1.96
Standard Error = 0.8/√150 = 0.8/12.25 = 0.065
Margin of Error = 1.96 × 0.065 = 0.13
CI = 4.2 ± 0.13 = (4.07, 4.33)
We're 95% confident the true satisfaction rating is between 4.07 and 4.33 out of 5.

Quick Fact

Jerzy Neyman (1894-1981), a Polish mathematician, formalized confidence interval theory in 1937. Before Neyman, statisticians used "fiducial inference" and other less rigorous approaches. Neyman showed that confidence intervals have a precise long-run frequency interpretation: over many repeated samples, a 95% CI method will capture the true parameter 95% of the time. His work with Egon Pearson also created the modern framework for hypothesis testing. Neyman's ideas transformed statistics from an art into a rigorous mathematical science.

Frequently Asked Questions

What does "95% confident" actually mean?

It means the method works 95% of the time in the long run. If you took 100 samples and computed 100 confidence intervals, about 95 would contain the true population mean. For any single interval, the true mean is either inside or outside – there's no probability about that specific interval. The 95% refers to the reliability of the method.

When should I use t-distribution instead of z-distribution?

Use the t-distribution when your sample size is small (n < 30) or when you don't know the population standard deviation and must estimate it from your sample. The t-distribution has fatter tails, giving wider (more conservative) intervals. For large samples (n ≥ 30), t and z give nearly identical results.

How do I make my confidence interval narrower?

Three ways: (1) Increase your sample size – this is usually the best option. Doubling your sample reduces the margin of error by about 30%. (2) Accept a lower confidence level (e.g., 90% instead of 95%). (3) Reduce variability in your measurements through better experimental design or more precise instruments.

What's the difference between standard deviation and standard error?

Standard deviation measures how spread out individual data points are. Standard error measures how much sample means vary from sample to sample. Standard error = standard deviation / √n. As your sample gets larger, the standard error shrinks (you estimate the mean more precisely), but the standard deviation stays the same (the population variability doesn't change).

Can a confidence interval include negative values?

Yes, if that makes sense for your measurement. Temperature differences, changes in weight, or profit/loss can all be negative. But if you're measuring something that can't be negative (like height or time), and your interval includes negative values, it suggests your estimate is very imprecise – the true value is probably close to zero.

What if my confidence interval is too wide to be useful?

A wide interval means you need more data. Calculate how large a sample you'd need for your desired margin of error: n = (Z × σ / E)², where E is your target margin of error. For example, to estimate a mean within ±2 units at 95% confidence with σ = 10, you need n = (1.96 × 10 / 2)² ≈ 96 samples.

Does a 99% CI mean I'm 99% sure the true mean is in my interval?

Not exactly – that's a common misconception. The true mean is a fixed value, not a random variable. Either it's in your interval or it isn't. The 99% refers to the method: if everyone used this method, 99% of their intervals would capture the true mean. Your specific interval either succeeded or failed; you just don't know which.

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