Free Z Score Calculator

A z-score calculator shows how many standard deviations a raw value is above or below the mean. Enter a value, mean, and standard deviation to see the formula steps, percentile, and normal-model tail areas.

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Standard score calculator

Enter the score and distribution

Try an example

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Z-score result

z = 2.00

The value is 2.00 standard deviations above the mean.

Percentile · area left

97.72%

Area to the right

2.28%

One tail beyond |z|

2.28%

Two tails beyond ±|z|

4.55%

Step-by-step work

  1. 1. Use z = (x - μ) / σ.
  2. 2. Substitute: (85.00 - 75.00) / 5.00.
  3. 3. Subtract: 10.00 / 5.00.
  4. 4. Divide: z = 2.00.

Percentile and tail values are estimates from a continuous normal distribution. A real data set may not follow that model.

How to Use the Z Score Calculator

1

Enter the distribution

Add the raw value, mean, and a standard deviation greater than zero.

2

Follow the formula

See subtraction and division substituted into z = (x - mean) / standard deviation.

3

Read the context

Use the labeled percentile and tail estimates only when a normal model is appropriate.

Frequently Asked Questions

What is a z-score calculator?

A z-score calculator shows how many standard deviations a value is above or below a distribution's mean using z = (x - mean) / standard deviation.

What does a positive or negative z-score mean?

A positive z-score is above the mean, a negative z-score is below the mean, and a z-score of zero is exactly at the mean.

How do I convert a z-score to a percentile?

Use the cumulative normal distribution. The resulting area to the left, multiplied by 100, is the approximate percentile under a normal model.

What is the difference between one-tailed and two-tailed probability?

A one-tailed probability measures outcomes beyond the score in one direction. A two-tailed probability includes equally extreme outcomes in both directions.

Can a z-score be greater than 3?

Yes. A z-score can have any real value, though scores beyond plus or minus 3 are uncommon in a normal distribution.