Ratio data is numerical data measured on a scale with a true zero, where zero means a complete absence of the quantity being measured. Because of that true zero, ratio data cannot be negative, and you can make meaningful ratio comparisons such as “twice as much” or “half as fast”. Common examples are height, weight, age, income, and reaction time.
Ratio data is the highest level of measurement, and the most informative. It supports the full range of statistical operations and is what most people picture when they think about quantitative research. Physical measurements, economic data, and most biological variables fall here.
Understanding what makes ratio data special, and where it differs from interval data, will sharpen both your analysis and your ability to interpret findings critically.
The Three Properties Of Ratio Data
- Ordered – you can rank observations from lowest to highest.
- Equal intervals – the difference between 10 and 20 represents the same quantity as between 50 and 60.
- True zero – zero means the complete absence of the attribute being measured, and no value can fall below it.
That true zero is what separates ratio from interval data, and it is what makes ratio statements (double, half, 30% more) mathematically meaningful. It is also why ratio data cannot take negative values — explained next.
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Examples Of Ratio Data
The most familiar ratio variables are everyday physical and economic measures — height, weight, age, and income — each with a genuine zero and no possible negative values. The table below shows common examples and confirms the true zero in each case.
| Variable | True Zero | Ratio Statement Valid | Example |
|---|---|---|---|
| Height (cm) | Yes – 0cm = no height | Yes | Average UK male height: 175.3cm (ONS / Health Survey for England) |
| Weight (kg) | Yes – 0kg = no mass | Yes | BMI calculations used in NHS health checks |
| Age (years) | Yes – 0 = birth | Yes | A 40-year-old is twice the age of a 20-year-old |
| Income (£) | Yes – £0 = no income | Yes | ONS ASHE median full-time earnings: £34,963 (2023) |
| Reaction time (ms) | Yes – 0ms = no elapsed time | Yes | Used in cognitive psychology research at UK universities |
| Number of GP visits | Yes – 0 = no visits | Yes | NHS Digital patient contact data |
| Temperature (Kelvin) | Yes – 0K = absolute zero | Yes | Used in physics; not in everyday UK weather reporting |
| Temperature (Celsius) | No – 0°C = water freezes | No | UK Met Office temperature data is interval, not ratio |
What Statistics Can You Use With Ratio Data?
Ratio data unlocks the full parametric toolkit, plus ratio-specific statistics that are not meaningful at lower levels. If you are unsure which procedure fits, our guide on which statistical test you should use walks through the decision by data type.
All Parametric Statistics
- Mean, median and mode, plus standard deviation and variance (see measures of variability).
- t-tests (independent and paired), ANOVA, and MANOVA.
- Pearson’s correlation and linear regression.
- All their extensions and variations.
Ratio-Specific Statistics
- Coefficient of variation (CV = standard deviation / mean × 100%) – expresses spread relative to the mean as a percentage. Only meaningful when the mean has a genuine scale (i.e. when data is ratio).
- Geometric mean – used with highly skewed ratio data, particularly in biological and economic research.
- Ratio comparisons – stating that Group A earns 1.8 times as much as Group B, or that response times in Condition X were 40% slower.
- Intervention group mean = 9,000 steps/day (SD = 2,000).
- Control group mean = 4,500 steps/day (SD = 1,800).
Because the data is ratio-level, the researcher can validly state that the intervention group walked 9,000 / 4,500 = 2 times as many steps as the control group — a ratio statement that would be meaningless on an interval scale. She reports means and SDs, runs an independent t-test, and adds Cohen’s d as an effect size. She also computes the coefficient of variation for the intervention group: CV = (2,000 / 9,000) × 100% = 22.2%, showing spread relative to the mean.
Ratio Data & Skewness
Ratio data is often skewed, particularly when it involves counts, incomes, or time measures. A positively skewed distribution (long right tail) is common: most values are relatively low, with a few very high outliers pulling the mean upward. When this happens:
- Report the median alongside the mean (or instead of it) to give a clearer picture of the typical value.
- Consider log transformations to normalise the distribution before parametric analysis.
- Use the IQR alongside the standard deviation to describe spread.
- Check for outliers systematically and decide in advance whether to include, exclude, or transform them.
| Scenario | Recommended Approach |
|---|---|
| Ratio data, approximately normal | Report mean and SD. Use parametric tests. |
| Ratio data, positively skewed (e.g. income, reaction times) | Report median and IQR alongside mean. Consider a log transform before parametric tests, or use non-parametric equivalents. |
| Count data (integers, often 0) | Consider Poisson regression or negative binomial regression instead of ordinary linear regression. |
| Time-to-event data (e.g. days until readmission) | Consider survival analysis methods (e.g. Kaplan-Meier, Cox regression). |
Can Ratio Data Be Negative?
No — ratio data cannot be negative. The defining feature of a ratio scale is a true (absolute) zero that represents the total absence of the quantity, so there is nothing below zero to measure. You cannot have negative height, negative weight, negative age, or a negative number of GP visits. The smallest possible value is always zero.
This is exactly where ratio data parts company with interval data. Interval scales have an arbitrary zero, not a true one, so they can go negative. Temperature in Celsius is the classic case: 0°C is just the freezing point of water, not “no temperature”, so −5°C is perfectly valid — and that is why Celsius is interval, not ratio.
The Four Levels Of Measurement Compared
Ratio is the top of a four-rung ladder of measurement. Each level adds a property to the one below it, and ratio is the only level with a true zero. For the full picture, see our guide to the levels of measurement.
| Level | Ordered? | Equal intervals? | True zero? | Can be negative? | Example |
|---|---|---|---|---|---|
| Nominal | No | No | No | N/A (labels only) | Blood type, gender |
| Ordinal | Yes | No | No | N/A (ranks) | Likert scale, exam grades |
| Interval | Yes | Yes | No | Yes | Temperature (°C), IQ score |
| Ratio | Yes | Yes | Yes | No | Height, weight, age, income |
