📏 Range Calculator

Range measures the total spread from minimum to maximum, while IQR measures the spread of the middle 50% of data. IQR is more robust because it's not affected by extreme outliers, making it a better measure of variability for skewed distributions.

In a box plot, the box represents the IQR (middle 50% of data), with the line inside showing the median. The "whiskers" extend to the minimum and maximum values (excluding outliers). Outliers are shown as individual points beyond the whiskers. A longer box or whiskers indicates greater variability.

IQR is resistant to outliers and extreme values, making it more reliable for comparing the typical spread of different datasets. A single extreme value can dramatically affect the range but has minimal impact on the IQR, providing a more stable measure of variability.

A small IQR indicates that the middle 50% of your data is tightly clustered around the median, suggesting low variability in the central portion of your dataset. This often indicates consistency or homogeneity in your data.

While you can calculate range and quartiles with as few as 4-5 data points, more data points (typically 20+) provide more reliable and meaningful statistics. Larger datasets give better representations of the underlying distribution and more accurate quartile estimates.

Not necessarily. Outliers can represent important information (like rare events or measurement errors). Investigate why outliers exist before removing them. If they're due to data entry errors, remove them. If they're legitimate extreme values, consider reporting statistics both with and without outliers.

Quartiles are specific percentiles: Q1 is the 25th percentile, Q2 (median) is the 50th percentile, and Q3 is the 75th percentile. Percentiles divide data into 100 equal parts, while quartiles divide it into 4 equal parts. Both describe the position of values within a distribution.