Z-Score Calculator

Calculate Z-scores, percentiles, and probabilities from the standard normal distribution.

Z-Score Calculations
Standardization: Convert a raw score to a Z-score (standard score).
The value you want to standardize
Population or sample mean
Population or sample standard deviation
Probability: Find the area under the normal curve for a given Z-score.
Standard score (positive or negative)
Which area to calculate
Inverse Normal: Find the Z-score for a given percentile/probability.
Enter percentile (e.g., 95 for 95th percentile)
Denormalization: Convert a Z-score back to the original scale.
Standard score to convert
Population or sample mean
Population or sample standard deviation
Results

Select a calculation mode and enter your values to see results.

Understanding Z-Scores & the Standard Normal Distribution
What is a Z-Score?

A Z-score (standard score) measures how many standard deviations a data point is from the mean. It standardizes values from different distributions onto a common scale, making comparisons possible.

Formula: Z = (x - μ) / σ

Why Use Z-Scores?

Standardization: Compare values from different distributions (e.g., SAT vs ACT scores)

Outlier Detection: Identify unusual values (typically |Z| > 3)

Probability: Calculate probabilities using the standard normal distribution

Hypothesis Testing: Foundation for many statistical tests

The 68-95-99.7 Rule (Empirical Rule)

For normal distributions:
68% of data falls within ±1 standard deviation (|Z| ≤ 1)
95% of data falls within ±2 standard deviations (|Z| ≤ 2)
99.7% of data falls within ±3 standard deviations (|Z| ≤ 3)

Common Z-Scores & Percentiles
Z-Score Percentile Interpretation
-3.0 0.13% Extremely low
-2.0 2.28% Significantly low
-1.0 15.87% Below average
0.0 50% Mean/Average
1.0 84.13% Above average
1.645 95% 90% CI critical value
1.96 97.5% 95% CI critical value
2.0 97.72% Significantly high
2.576 99.5% 99% CI critical value
3.0 99.87% Extremely high
Interpreting Z-Scores
Positive Z-Score

Value is above the mean

Example: Z = 1.5 means the score is 1.5 standard deviations above average

Negative Z-Score

Value is below the mean

Example: Z = -1.5 means the score is 1.5 standard deviations below average

Zero Z-Score

Value equals the mean

Example: Z = 0 means the score is exactly average

Real-World Applications
Standardized Testing: SAT, ACT, IQ scores use Z-scores to compare performance
Quality Control: Manufacturing uses Z-scores to detect defects
Finance: Stock returns are standardized for risk comparison
Medical: Growth charts, lab results use percentiles from Z-scores
Research: Standardizing variables before analysis
Normal Distribution Visualization
Standard normal distribution with key Z-scores marked
Key Formulas
Z-Score: Z = (x - μ) / σ
Raw Score: x = μ + Z × σ
Percentile: P = Φ(Z) × 100%
Z from Percentile: Z = Φ⁻¹(P/100)

Common Mistakes to Avoid
Wrong Distribution:
Z-scores assume normal distribution. Non-normal data needs transformation first.
Sample vs Population:
Use sample SD (s) for sample data, population SD (σ) for population data.
Interpretation:
Z-score tells position relative to mean, not absolute quality or performance.

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Z‑Score Calculator: Frequently Asked Questions

What is a Z‑score?

A Z‑score (standard score) tells how many standard deviations a value is above or below the mean: Z = (x − μ) ÷ σ. Positive Z means above average; negative Z means below.

How do I convert a raw score to a Z‑score?

Enter the raw score, mean (μ), and standard deviation (σ), then click Calculate under the “Score → Z” tab. The tool also shows the percentile and tail probabilities.

When should I use Z‑scores?

Use Z‑scores to compare values from the same normal (or approximately normal) distribution, find tail probabilities, or convert between scores and percentiles.

What’s the difference between left, right, and two‑tail probabilities?

Left tail is P(Z ≤ z), right tail is P(Z ≥ z), and two‑tail often refers to the probability outside ±|z|. Choose the area type that matches your hypothesis or interpretation.