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  1. Home
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  3. /Statistics for Biology
  4. /Confidence Interval Calculator

Confidence Interval Calculator

Last updated: February 24, 2026

Calculator

Results

Standard Error

1.917029

Margin of Error

3.757377

Confidence Interval Lower Bound

71.542623

Confidence Interval Upper Bound

79.057377

Interval Width

7.514753

Relative Margin (%)

4.9899

%

Results

Standard Error

1.917029

Margin of Error

3.757377

Confidence Interval Lower Bound

71.542623

Confidence Interval Upper Bound

79.057377

Interval Width

7.514753

Relative Margin (%)

4.9899

%

The Confidence Interval Calculator computes the range within which the true population mean is likely to fall, based on sample statistics. A confidence interval provides more information than a point estimate alone, expressing both the best estimate of the parameter and the uncertainty around it.

A 95% confidence interval means that if the experiment were repeated many times, 95% of the calculated intervals would contain the true population mean.

Visual Analysis

How It Works

The confidence interval is calculated as:

CI = Mean ± z × (SD / √n)

  • Mean = sample mean
  • z = z-value for desired confidence level (1.96 for 95%, 2.576 for 99%)
  • SD / √n = standard error of the mean (SEM)
  • Margin of Error = z × SEM

Worked Examples

95% CI for Enzyme Activity

Inputs

mean val75.3
std dev10.5
n30
z value1.96

Results

ci lower71.54
ci upper79.06
margin error3.76
se1.92

We are 95% confident the true mean enzyme activity is between 71.54 and 79.06 units.

99% CI for Body Temperature

Inputs

mean val37.1
std dev0.5
n100
z value2.576

Results

ci lower36.97
ci upper37.23
margin error0.13
se0.05

With 100 measurements, the 99% CI is very narrow, reflecting high precision.

Frequently Asked Questions

It means that if you collected 100 different random samples and computed a CI from each, approximately 95 of those intervals would contain the true population mean.

95% confidence is standard in most biological research. Use 99% when you need higher confidence in safety-critical applications.

Increase sample size, reduce variability through better experimental control, or accept a lower confidence level.

Sources & Methodology

Zar, J.H. Biostatistical Analysis. Cumming, G. Understanding the New Statistics.
R

Roboculator Team

The Roboculator Team explains calculations, planning tools, and practical formulas in clear language for real-life situations.

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