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Empirical Rule Calculator

Apply the 68-95-99.7 rule to any mean and standard deviation

Updated · Free, no signup

See how many standard deviations this value is from the mean.

68% of values fall between

85 to 115

95% of values fall between

70 to 130

99.7% of values fall between

55 to 145

μ − 1σ

85

μ + 1σ

115

μ − 2σ

70

μ + 2σ

130

μ − 3σ

55

μ + 3σ

145

z-score of your value

2

Share of values below your value

97.725%

Exact normal-distribution percentile.

  • 130 is 2 standard deviations above the mean; about 97.72% of values are lower.

Normal curve with 1σ, 2σ and 3σ bands

Empirical rule bands

BandRangeEmpirical ruleExact normal %Outside (each tail)
μ ± 1σ85 to 11568%68.27%15.87%
μ ± 2σ70 to 13095%95.45%2.28%
μ ± 3σ55 to 14599.7%99.73%0.13%

About the Empirical Rule Calculator

This empirical rule calculator applies the 68-95-99.7 rule (also called the three-sigma rule) to a normal distribution. Enter the mean and standard deviation and it gives the ranges that contain about 68% of values (within one standard deviation), 95% (within two) and 99.7% (within three), with a bell-curve chart marking each band.

Students use it for statistics homework, teachers to interpret test scores, and analysts for quick sanity checks — for example, to judge whether a measurement is unusual, or to set rough quality-control limits. Optionally enter a specific value to see its z-score and the exact percentage of a normal distribution that falls below it.

The rule is an approximation that holds for data that are roughly bell-shaped and symmetric. For skewed data, Chebyshev’s inequality gives a guaranteed (but looser) bound: at least 75% of any distribution lies within two standard deviations, and at least 88.9% within three.

How to use the empirical rule calculator

  1. 1Enter the mean of your data.
  2. 2Enter the standard deviation.
  3. 3Read the ranges holding 68%, 95% and 99.7% of values.
  4. 4Optionally enter a value to see its z-score and percentile.
  5. 5Use the chart to visualise where each band sits on the bell curve.

Formula and method

P(μ − σ < X < μ + σ) ≈ 68%; P(μ − 2σ < X < μ + 2σ) ≈ 95%; P(μ − 3σ < X < μ + 3σ) ≈ 99.7%

For a normal distribution with mean μ and standard deviation σ, the proportions inside each band depend only on how many standard deviations wide the band is. The exact figures are 68.27%, 95.45% and 99.73%, which the empirical rule rounds to 68, 95 and 99.7. Each band is found by simply adding and subtracting k × σ from the mean.

The z-score z = (x − μ)/σ says how many standard deviations a value lies from the mean, and the percentage below it is the standard normal cumulative probability Φ(z). The rule applies to data that are approximately normal; heavily skewed data or data with outliers can differ substantially.

μ
Mean of the distribution
σ
Standard deviation
z
z-score: (x − μ) ÷ σ
Φ(z)
Standard normal cumulative probability

Worked examples

IQ scores (mean 100, SD 15)

About 68% of people score between 85 and 115, 95% between 70 and 130 and 99.7% between 55 and 145. A score of 130 is 2 standard deviations above the mean, higher than about 97.7% of people.

Adult height (illustrative: mean 69 in, SD 3 in)

One SD either side gives 66–72 inches (68%), two SDs 63–75 inches (95%) and three SDs 60–78 inches (99.7%). A height of 74 inches is about 1.67 SDs above the mean, taller than roughly 95.2% of the group if heights are normally distributed.

Machine fill weights (mean 500 g, SD 4 g)

Nearly all fills (99.7%) should weigh between 488 g and 512 g. A 488 g pack is exactly 3 standard deviations low — only about 0.135% of packs should be that light or lighter, so it may signal a problem.

Frequently asked questions

What is the empirical rule?+

The empirical rule (68-95-99.7 rule) states that for a normal distribution about 68% of values lie within one standard deviation of the mean, 95% within two, and 99.7% within three. It is a quick way to judge how typical or unusual a value is.

What are the exact percentages?+

The exact normal-distribution values are 68.27% within 1σ, 95.45% within 2σ and 99.73% within 3σ. The empirical rule rounds these to 68%, 95% and 99.7% for easy mental math.

When can I use the empirical rule?+

Use it when your data are approximately normal — unimodal, symmetric and bell-shaped, like heights, test scores or measurement errors. For skewed data such as incomes, use Chebyshev’s theorem or the actual percentiles instead.

What percentage of data is above one standard deviation?+

About 16%. Since 68% lies within ±1σ, the remaining 32% is split equally between the two tails, so roughly 16% is above μ + σ and 16% is below μ − σ. Above μ + 2σ is about 2.5%.

How is the empirical rule different from Chebyshev’s theorem?+

The empirical rule applies only to normal (bell-shaped) data and gives approximate percentages. Chebyshev’s theorem applies to any distribution and gives minimums: at least 1 − 1/k² of values lie within k standard deviations — 75% within 2σ and 88.9% within 3σ.

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