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Enter value, mean, and standard deviation.
A z-score, or standard score, tells you how many standard deviations a value is from the mean. It is calculated as z = (x − μ) / σ, where x is the value, μ is the mean, and σ is the standard deviation. A z-score of 0 equals the mean; +1 is one standard deviation above it.
Positive z-scores are above the mean, negative ones below. In a normal distribution, about 68% of values fall within ±1, 95% within ±2, and 99.7% within ±3. Z-scores let you compare values from different datasets on the same scale.
z = (x − μ) / σ — the value minus the mean, divided by the standard deviation.
The value is below the mean. A z-score of −1.5 is 1.5 standard deviations below average.
Values beyond ±2 or ±3 are uncommon in a normal distribution and are often treated as outliers.
No. Dividing by zero is undefined, so the SD must be greater than zero.
Related but not identical. A z-score maps to a percentile via the normal distribution's cumulative function.
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