Log Normal Calculator

Log Normal Calculator: run log normal calculations online. Formula, assumptions, and interpretation guide.

A Z-score (standard score) measures how many standard deviations a data point is from the mean of its distribution. A Z-score of 0 means the value equals the mean; +2 means it is two standard deviations above the mean; −1.5 means it is 1.5 standard deviations below. Z-scores allow direct comparison of values from different distributions and are fundamental to hypothesis testing, quality control, and standardised testing.

Z-scores are used in SAT/ACT scoring, credit risk modelling (Altman Z-score), manufacturing quality control (Six Sigma), and converting between raw test scores and percentile ranks.

Enter the raw data value (x) you want to standardise.
Enter the population or sample mean (μ or x̄).
Enter the standard deviation (σ or s).
The Z-score is calculated and the corresponding percentile rank shown.
Use the Z-score table or the percentile output to interpret where the value falls in the distribution.

Z-score formula

Z = (x − μ) / σ

Where x = observed value, μ = population mean, σ = standard deviation.

Percentile: use the standard normal cumulative distribution function Φ(Z) — a Z of +1.645 corresponds to the 95th percentile; Z of −1.96 corresponds to the 2.5th percentile.

Interpreting your Z-score

Z-score to percentile reference

Z = 0: 50th percentile (average). Z = ±1: 84th/16th percentile. Z = ±2: 97.7th/2.3rd percentile. Z = ±3: 99.87th/0.13th percentile. In quality control (Six Sigma), a process operating at 6σ produces fewer than 3.4 defects per million opportunities. A Z-score outside ±2 is generally considered statistically unusual at the 5% significance level.

Statistics tips and best practices

Z-scores assume a roughly normal distribution — they are less meaningful for heavily skewed data.
Use Z-scores to compare a student's performance across different tests that use different scales.
In finance, Z-scores are used to compare returns across assets with different means and volatilities (Sharpe ratio is a type of Z-score).
For sample data, use the sample mean and sample standard deviation rather than population parameters.

The SAT is designed so the mean score is 1000–1060 with a standard deviation of approximately 200 — a score of 1400 corresponds to roughly a Z of +1.7 (about 95th percentile).
The Altman Z-score for predicting corporate bankruptcy uses a weighted combination of five financial ratios — scores above 2.99 indicate financial health; below 1.81 indicates distress.
In Six Sigma quality programmes, "six sigma" quality means the process mean is six standard deviations from the nearest specification limit.

Common mistakes to avoid

Using population standard deviation when you only have sample data — use sample SD (divide by n−1) for observed samples.
Interpreting Z-scores from non-normal distributions as percentiles — the percentile conversion only works reliably for normal distributions.
Forgetting that Z-scores are unitless — comparing Z-scores across different scales is valid, but comparing raw values from different scales is not.

Z-score outputs are statistical summaries for educational and analytical purposes. They do not constitute actuarial, medical, or financial assessments. Standardised test scores are owned and interpreted by the respective testing organisations.