Enter a set of numbers in the data input field (comma-separated or space-separated) and get the range — the difference between the maximum and minimum values — along with min, max, mean, median, mode, and count of your dataset. Great for quick statistical summaries of any number set.
Range
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Minimum
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Maximum
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Mean (Average)
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Median
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Mode
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Total Numbers
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The range is a measure of spread in a dataset. It tells you how far apart the largest and smallest values are. A large range indicates high variability, while a small range suggests the data points are clustered closely together.
Calculating the range is straightforward: subtract the minimum (smallest) value from the maximum (largest) value. The formula is Range = Max − Min. For example, for the dataset {2, 5, 9, 14}, the range is 14 − 2 = 12.
No, the range can never be negative. Since you always subtract the smallest value from the largest, the result is always zero or a positive number. A range of zero means all values in the dataset are identical.
The range considers all values from minimum to maximum and is sensitive to outliers. The interquartile range (IQR) measures only the spread of the middle 50% of data (Q3 − Q1), making it more robust against extreme values. Use IQR when your dataset has outliers that might skew the range.
You can enter numbers separated by commas (e.g. 5, 10, 15, 20) or spaces (e.g. 5 10 15 20). Both positive and negative numbers are supported. The calculator will ignore any extra whitespace or blank entries automatically.
The mean is the arithmetic average of all values. The median is the middle value when data is sorted — it's less affected by outliers than the mean. The mode is the value that appears most frequently in the dataset. Together these three measures describe the central tendency of your data.
The range only uses two data points — the maximum and minimum — so a single extreme outlier can dramatically inflate it and give a misleading picture of spread. Statistical measures like standard deviation or IQR consider all data points and are generally more reliable for describing variability in a dataset.
The range is used in everyday contexts such as analyzing temperature fluctuations over a week, assessing salary spreads in a company, evaluating test score distributions in a class, or comparing product quality measurements in manufacturing. It gives a quick snapshot of how spread out your data is.