Normal Distribution Calculator
Find the probability density and cumulative probability for a normal value.
Results update as you type.
About this calculator
The normal (Gaussian) distribution is the classic symmetric bell curve that describes heights, measurement errors, test scores and countless other natural quantities. Given a value x, a mean μ and a standard deviation σ, this calculator returns the probability density at x, the cumulative probability P(X ≤ x) (the area under the curve to the left), the right-tail probability P(X ≥ x) and the equivalent z-score, and plots the curve.
Internally it converts x to a z-score, z = (x − μ) / σ, then evaluates the standard-normal cumulative distribution with an accurate error-function approximation. Enter an optional upper bound x₂ to get the range probability P(x ≤ X ≤ x₂), computed as the difference of the two cumulative values.
Worked example: for a distribution with μ = 100 and σ = 15, a value of x = 120 has a z-score of (120 − 100) ÷ 15 ≈ 1.33, so P(X ≤ 120) is about 90.9% and the right tail P(X ≥ 120) about 9.1%. This is the tool behind IQ scores, control charts and the empirical 68–95–99.7 rule.
Frequently asked questions
What is the difference between the pdf and the cdf?
The probability density (pdf) is the height of the bell curve at x; it is not a probability by itself. The cumulative distribution (cdf) is the area under the curve up to x, which gives the probability of a value at or below x.
Why is the probability of an exact value zero?
For a continuous distribution like the normal, any single exact point has zero probability — only ranges have non-zero probability. That is why we report the density at x and the cumulative area rather than P(X = x).
What is the 68–95–99.7 rule?
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 unusual a value is without computing the exact area.
How do I find the probability between two values?
Enter the lower value as x and the upper value as x₂; the calculator returns P(x ≤ X ≤ x₂) by subtracting the cumulative probability at the lower point from the one at the upper point.
What is the difference between the normal distribution and the standard normal?
The standard normal is just the normal distribution with a mean of 0 and a standard deviation of 1. Converting any value to its z-score maps it onto the standard normal, which is how this tool computes probabilities for any μ and σ.
Does my data have to be perfectly normal?
No real data is exactly normal, but the results are reliable when the data is roughly bell-shaped and symmetric. For strongly skewed or heavy-tailed data the probabilities are only approximate.
API — use this calculator from code
Call this calculator as a free JSON endpoint — no key required. Send the field values below as query parameters or JSON. Read the full API docs →
Endpoint
GET https://calculator.free/api/v1/normal-distribution/curl
curl "https://calculator.free/api/v1/normal-distribution/?x=120&mean=100&sd=15"JavaScript fetch()
const r = await fetch(
"https://calculator.free/api/v1/normal-distribution/?" + new URLSearchParams({
"x": "120",
"mean": "100",
"sd": "15"
}));
const data = await r.json();
console.log(data.results);Results are estimates for general guidance only, not financial, medical or tax advice.