Statistics

Z-Score Calculator — Standard Score & Percentile

Enter a value, mean, and standard deviation to instantly find the z-score (how many standard deviations from the mean) and its percentile under a normal distribution.

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Enter value, mean and standard deviation to see the z-score and percentile
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Disclaimer: This tool performs standard mathematical calculations for educational and reference purposes. Always double-check results independently for graded coursework, exams, or safety-critical applications.

What a Z-Score Measures

A z-score (also called a standard score) converts a raw value into a measurement of how many standard deviations it sits away from the mean of its data set. A z-score of 0 means the value is exactly average. A z-score of +2 means the value is two standard deviations above the mean, while −1.5 means it's one and a half standard deviations below average. Because z-scores strip away the original units, they let you compare values from completely different scales — test scores, heights, temperatures — on the same standardized footing.

From Z-Score to Percentile

Once you have a z-score, you can translate it into a percentile by assuming the underlying data follows a normal (bell-curve) distribution. The percentile tells you roughly what share of the distribution falls below your value. This calculator uses a well-known rational polynomial approximation of the standard normal cumulative distribution function (CDF) to compute that percentile directly in your browser, without needing lookup tables.

Worked Example

Suppose a student scores x = 85 on a test where the class mean is μ = 75 and the standard deviation is σ = 10.

  1. Step 1: z = (x − μ) / σ = (85 − 75) / 10 = 1.0
  2. Step 2: A z-score of 1.0 falls at roughly the 84th percentile under a standard normal distribution — meaning the student scored better than about 84% of a normally distributed population.

Frequently Asked Questions

A z-score tells you how many standard deviations a value sits above or below the mean. A z-score of 0 means the value equals the mean exactly; positive means above average, negative means below average, and the magnitude tells you how unusual the value is.
As a rough rule of thumb under a normal distribution, values within z = ±1 cover about 68% of the data, within ±2 cover about 95%, and within ±3 cover about 99.7%. So a z-score beyond about ±2 is generally considered notably far from typical, and beyond ±3 is quite rare.
The percentile conversion relies on the standard normal (bell curve) distribution, which is the most common assumption in statistics for naturally occurring data like test scores or measurement errors. If your actual data is heavily skewed or doesn't follow a bell curve, the raw z-score is still meaningful, but the percentile figure may not match the true percentile in your specific data set.
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