For example, an extremely small or extremely large value in a dataset will not affect the calculation of the IQR because the IQR. However, the interquartile range and standard deviation have the following key difference: The interquartile range (IQR) is not affected by extreme outliers. Outliers here are defined as observations that fall below Q1 1.5 IQR or above Q3 + 1.5 IQR. This gives an indication of the ‘spread’ of the data either side of the median. Both metrics measure the spread of values in a dataset. The interquartile range is often used to find outliers in data. It then finds the median of the upper half (Upper Quartile) and subtracts the median of the lower half (Lower Quartile) to produce the difference between the quarter and three-quarters value – known as the Interquartile Range. This statistical measure uses the concept of the ‘median’ rather than the mean – the middle-ranking value in a range of data ranked from largest to smallest.

But this can give an inaccurate interpetation if we then assume the pebbles on the two beaches are similar the ‘spread’ of pebbles on one beach, from very small to very large may, in fact, be quite different from another beach where the pebble sizes are all very close to the ‘mean’. We may use, for example, the ‘mean’ pebble size we have measured on a beach to compare with the ‘mean’ of another beach. You can use this interquartile range calculator to determine the interquartile range of a set of numbers, including the first quartile, third quartile, and median. For example, the IQR of this data set is 20 because 50 30 20 50 30 20. To find the interquartile range we subtract the first quartile from the third quartile. So, 25 of the data sit below Q1, and 75 of the data sit above Q1. The first (or lower) quartile (Q1) marks the center of the lowest half of a data set. To use the interquartile range to identify outliers, you can use a rule called the '1.5xIQR rule.'. Definition: Interquartile Range (IQR) The interquartile range is one way to measure how spread out a data set is. The interquartile range (or IQR) of a data set is a measure of how the data values are spread around the center of the data set. You can then explain it by saying that half the sample readings were between these two values, a quarter were smaller than the lower quartile, and a quarter higher than the upper quartile. You should always report both numbers, not just the difference between them. It is calculated by finding the difference between the upper quartile (Q3) and the lower quartile (Q1). The interquartile range is an interval, not a scalar. When we need to describe data collected from an area to compare with data from another area, we may use some sort of ‘average’ to summarise it. The interquartile range (IQR) is a measure of the spread or dispersion of the middle 50 of the data. It is one of a number of ‘measures of dispersion’. The interquartile range (QR) is a measure of ‘spread’ in a collection of data. Key fact The interquartile range is the difference between the lower quartile and the upper quartile.

Interquartile range