Random Height Generator

Generate a random human height within a realistic range, in cm and ft/in.

Population

Count

Generated: 0Avg Height: 0 cm

How the random height generator works

Real human height follows a roughly bell-shaped (normal) distribution rather than a flat range, so this tool samples from a normal distribution centered on commonly-cited adult averages — about 175 cm for adult males and 162 cm for adult females — using the Box-Muller transform driven by your browser's cryptographically secure random source (crypto.getRandomValues()). Results are clamped to a realistic min/max so you never get an implausible outlier, then converted to feet and inches alongside the centimeter value. Handy for generating placeholder character stats, test data for forms, or random profile fields. Everything runs locally in your browser — nothing is sent anywhere.

Built and maintained by Meet Shah · Last updated 19 July 2026

What this tool is used for

Populating a test dataset with plausible body measurements.
Producing sample data for a chart or a dashboard mockup.
Generating input to exercise unit conversion between metric and imperial.
Filling a demo profile set with realistic-looking values.
Testing how a UI renders a range of values rather than one.

Frequently Asked Questions

Are the heights realistic or uniform?: Human height follows a normal distribution, not a uniform one — roughly 68% of adults fall within one standard deviation of the mean. A uniform draw between two bounds produces implausible test data with far too many extremes.
What are typical adult means?: Global adult averages sit near 171 cm for men and 159 cm for women, with a standard deviation around 7 cm, though national figures vary widely — the Netherlands is roughly 10 cm above the global mean. Pick bounds that match the population you are modelling.
Why not just use a random number generator?: Because the units and the distribution both carry meaning. Height data needs cm/ft-in conversion, sensible precision (nobody records 173.4829 cm), and a realistic spread — which is exactly what a generic number generator does not give you.
How do I convert between metric and imperial?: One inch is exactly 2.54 cm by international agreement, so a foot is exactly 30.48 cm. Because those are exact definitions rather than measurements, conversions round-trip cleanly to the precision you choose.
What is this useful for?: Seeding test databases, demoing health apps, populating fitness or medical UI mockups, and load-testing forms with values that pass realistic validation rules. Using implausible data hides bugs that only appear at real-world values.
Are the values drawn from a realistic distribution?: Only if the generator uses one. Human height is close to normally distributed, so a uniform draw over a range produces far too many extremes — which is fine for layout testing and wrong for anything statistical.

Common errors and gotchas

Treating the distribution as authoritative, when it is a simplified bell curve rather than population data.
Ignoring that height distributions differ substantially by population and by sex.
Using generated values in anything presented as real data.
Mixing units within one dataset, which produces implausible records.
Assuming the range covers edge cases, when a bell curve rarely produces the extremes you want to test.