Range is the simplest measure of spread in descriptive statistics, calculated as Maximum minus Minimum. It tells you the total width of your dataset — how far apart the smallest and largest values are. Use range when you need a quick, easy-to-understand summary of variability. For example, a wildlife biologist might report that body weights of sampled animals ranged from 4.2 kg to 18.7 kg, giving an immediate sense of size variation in the population.

The range formula in statistics is: Range = Maximum value − Minimum value. To apply it: (1) list all values in your dataset, (2) identify the largest value (maximum), (3) identify the smallest value (minimum), (4) subtract minimum from maximum. For example, with data {5, 12, 3, 19, 7}, the range is 19 − 3 = 16.

Range tells you the total spread of your data — the distance between the smallest and largest observed values. A large range indicates high variability (data points are spread far apart); a small range indicates low variability (data points cluster closely together). However, range only describes the extremes and says nothing about how values are distributed between them.

The main limitations of range as a measure of dispersion are: (1) Outlier sensitivity — a single extreme value can massively inflate range, making it misleading; (2) Ignores distribution — range tells you nothing about how values are spread between the extremes; (3) Not robust — unlike IQR or standard deviation, range becomes less reliable as sample size increases because more extreme values are likely to appear. Always pair range with IQR or SD for a complete picture.

Range covers the full spread from minimum to maximum — it is highly sensitive to outliers. IQR (Interquartile Range = Q3 − Q1) covers only the middle 50% of data, making it robust to outliers. Use range for a quick overall spread estimate; use IQR when your data contains extreme values or is skewed. For example, if exam scores are {20, 55, 60, 65, 70, 99}, the range is 79 but the IQR is only 10, correctly reflecting that most scores are tightly clustered.

Outliers can dramatically inflate the range, making it appear that data is far more variable than it actually is. For instance, if reaction times are mostly between 200–400 ms but one participant recorded 1,200 ms, the range becomes 1,000 ms instead of 200 ms. This is why range should be reported alongside IQR when outliers are suspected. This calculator flags potential outliers using Tukey Fences (Q1 − 1.5×IQR and Q3 + 1.5×IQR).

Step-by-step: (1) List all values in your dataset. (2) Sort them from smallest to largest. (3) Identify the maximum (last value). (4) Identify the minimum (first value). (5) Calculate: Range = Maximum − Minimum. Example: Data = {34, 12, 78, 45, 19, 56}. Sorted: {12, 19, 34, 45, 56, 78}. Min = 12, Max = 78. Range = 78 − 12 = 66.

In APA 7th edition, report the range alongside mean and SD: "Scores ranged from 45 to 98 (M = 72.3, SD = 14.6, Range = 53)." Always state both the minimum and maximum values explicitly, not just the range number. For journal articles, include range in your descriptive statistics table alongside M, SD, skewness, and kurtosis. See the Reporting Examples section on this page for five detailed templates.

This tool is designed for educational use and exploratory analysis. For formal research submissions, always verify results with peer-reviewed statistical software (R, Python, SPSS, or SAS). Citation for this tool: STATS UNLOCK. (2025). Descriptive statistics range calculator. Retrieved from https://statsunlock.com/descriptive-statistics-range-calculator. The values computed here are mathematically verified and suitable for learning, teaching, and initial data exploration.

There is no universal "good" range — it depends entirely on the scale and context of your data. A temperature range of 5°C may be large for a controlled lab but tiny for seasonal outdoor temperatures. A useful benchmark: if the range is greater than approximately 4–6 times the standard deviation, extreme outliers are likely present. Compare your range to the IQR: if Range ≫ IQR, outliers are inflating the spread. Context-specific benchmarks from domain literature are the most reliable guide.