BCBetter Calculators

Range of Numbers Calculator

Calculate the range (difference between the largest and smallest values) of up to six numbers.

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Enter your values and click Calculate

How It Works

The calculator scans all six input values to identify the largest (maximum) and the smallest (minimum). It then subtracts the minimum from the maximum to compute the range: Range = Max − Min. The average (mean) is also displayed as the sum of all six values divided by six. Both the range and the average together give you a quick sense of how spread out and how central your data is. Range answers the question 'how wide is my dataset?' while the average answers 'where is the centre?' Comparing the two reveals important patterns — for example, a large range alongside an average close to one extreme often signals the presence of an outlier pulling one end of the data. All six values are always included in the calculation, so ensure every field contains a valid number before interpreting the results.

Examples

Test scores
Scores of 55, 70, 80, 85, 90, 95.
Result: Range = 95 − 55 = 40.
All equal values
Six values all equal to 50.
Result: Range = 0 (no spread).
Temperatures across a week
Daily highs of 62, 65, 71, 78, 80, 74 degrees.
Result: Range = 18 degrees — moderate weekly temperature variability.

Frequently Asked Questions

Is a larger range always bad?
Not necessarily — a larger range simply indicates greater variability in your data. Whether that variability is a problem depends entirely on context. In manufacturing, a small range often indicates consistent quality, while in finance a large range may signal higher volatility.
How is range different from standard deviation?
Range only uses the two extreme values (max and min) and ignores everything in between. Standard deviation accounts for every data point and measures how far each one deviates from the mean, giving a more complete and reliable picture of overall spread.
What does an average of zero range tell me?
A range of zero means all six values are identical, indicating there is no variability at all in your dataset. This can be a useful sanity check — if you expect variation but see a range of zero, double-check that your inputs are correct.

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