Z-score Calculator

Calculate Z-scores, percentiles, and probabilities for normal distribution

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Input Values

Enter known values

The actual observed value

Average of the dataset

Measure of data spread

Results

Z-score
1.0000
Percentile
84.13%
Probability
0.8413
Interpretation
Average (Normal Range)
Formula
Z = (X - μ) / σ

Z-Score Calculator: Calculate Standard Scores Online

Calculate Z-scores, percentiles, and probabilities instantly with our free online Z-score calculator. A Z-score (standard score) measures how many standard deviations a value is from the mean, making it essential for statistical analysis, standardized testing, and data comparison.

What is a Z-Score?

A Z-score (also called a standard score) is a statistical measurement that describes a value's relationship to the mean of a group of values. It's measured in terms of standard deviations from the mean. If a Z-score is 0, it indicates that the data point's score is identical to the mean. A Z-score of 1.0 indicates a value that is one standard deviation from the mean.

Z-Score Formula

The Z-score formula is: Z = (X - μ) / σ

  • Z = Z-score (standard score)
  • X = Raw score (the value you're evaluating)
  • μ = Population mean
  • σ = Population standard deviation

Interpretation Guide

  • Z > 3: Very High (Exceptional) - 99.9%+ percentile
  • Z = 2 to 3: High (Above Average) - 95-99.9% percentile
  • Z = 1 to 2: Above Average - 84-95% percentile
  • Z = -1 to 1: Average (Normal Range) - 16-84% percentile
  • Z = -2 to -1: Below Average - 5-16% percentile
  • Z = -3 to -2: Low - 0.1-5% percentile
  • Z < -3: Very Low (Exceptional) - <0.1% percentile

Common Uses of Z-Scores

  • Standardized Testing: SAT, ACT, GRE, IQ tests use Z-scores to compare performance across different test versions
  • Research & Data Analysis: Compare data from different scales or distributions in social sciences, psychology, and medical research
  • Quality Control: Identify outliers and defects in manufacturing and production processes
  • Finance: Assess investment risk and identify unusual market movements
  • Sports Analytics: Compare athlete performance across different metrics and seasons
  • Medical Diagnosis: Evaluate test results and identify abnormal values

How to Use This Calculator

  1. Choose Calculation Mode: Select whether you want to calculate Z-score, raw score, or percentile
  2. Enter Known Values: Input your raw score, mean, and standard deviation (or percentile depending on mode)
  3. View Results: Get instant calculations including Z-score, percentile, and probability
  4. Interpret: Use the interpretation guide to understand what your Z-score means

Understanding Percentiles

A percentile indicates the percentage of scores that fall below a particular value. For example, if your Z-score corresponds to the 84th percentile, it means your score is higher than 84% of all scores in the distribution. The 50th percentile represents the median (mean in a normal distribution).

Normal Distribution

Z-scores are most commonly used with normal (Gaussian) distributions. In a normal distribution:

  • 68% of values fall within ±1 standard deviation (Z = -1 to 1)
  • 95% of values fall within ±2 standard deviations (Z = -2 to 2)
  • 99.7% of values fall within ±3 standard deviations (Z = -3 to 3)

Frequently Asked Questions

What does a negative Z-score mean?

A negative Z-score indicates that the raw score is below the mean. For example, a Z-score of -1.5 means the value is 1.5 standard deviations below the mean.

What is a good Z-score?

"Good" depends on context. For standardized tests, a Z-score above 1 (84th percentile) is generally considered above average. For quality control, values beyond ±2 or ±3 may indicate problems.

Can Z-scores be used for non-normal distributions?

While Z-scores can be calculated for any distribution, their interpretation (especially percentiles) is most accurate for normal distributions. For non-normal data, consider other standardization methods.

How do I convert between Z-score and percentile?

Use a Z-table or calculator (like this one) to convert. For example, Z = 1.0 corresponds to approximately the 84th percentile, and Z = -1.0 corresponds to the 16th percentile.