Range Calculator

Enter your dataset to calculate the range, minimum, maximum, and mid-range — with full step-by-step working and formulas.

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Understanding Range and Spread

The Range

The range is the simplest measure of spread (also called dispersion). It tells you how widely the values in a dataset are spread out.

Range = Max − Min

Example: Dataset: 4, 9, 2, 15, 7, 11

Sorted: 2, 4, 7, 9, 11, 15 → Range = 15 − 2 = 13

A large range indicates the data is widely spread. A small range means the values are clustered together.

The Mid-Range

The mid-range is the average of the maximum and minimum values. It gives a rough estimate of the centre of the data.

Mid-Range = (Max + Min) ÷ 2

Example: Dataset: 2, 4, 7, 9, 11, 15

Mid-Range = (15 + 2) ÷ 2 = 17 ÷ 2 = 8.5

Note: The mid-range is not the same as the median. The median considers all values; the mid-range only uses the two extremes.

Limitations of Range

The range is easy to calculate but has significant weaknesses as a measure of spread:

Limitation Example
Affected by outliers Dataset: 5, 6, 6, 7, 7, 100 → Range = 95, but most values are 5–7
Ignores all middle values Only uses max and min — tells nothing about how the middle data is distributed
Not suitable for open-ended data If a dataset says "60 or more" as a category, range cannot be calculated

For a more robust measure of spread, use Standard Deviation or the Interquartile Range (IQR).

Frequently Asked Questions

No. The range is always zero or positive because it is calculated as Maximum minus Minimum. Since the maximum is always greater than or equal to the minimum, the result cannot be negative. A range of 0 means all values in the dataset are identical.
The range is a quick and simple measure, but it is sensitive to outliers. A single extreme value can make the range very large even if most data is tightly clustered. For a more reliable measure of spread, use the Interquartile Range (IQR) or Standard Deviation.
The range uses the full spread of data (Max − Min). The IQR (Interquartile Range) uses only the middle 50% of data (Q3 − Q1), making it resistant to outliers. The IQR is generally considered a more reliable measure of spread for skewed or outlier-heavy datasets.