The interquartile range tells us the spread of the middle 50% of values in the dataset. The range tells us the difference between the largest and smallest value in the entire dataset.However, the range and interquartile range have the following difference: Both metrics measure the spread of values in a dataset.The range and interquartile range share the following similarity: Interquartile Range: Similarities & Differences The range tells us the spread of the entire dataset while the interquartile range tells us the spread of the middle half of the dataset. Interquartile Range = 3rd Quartile – 1st Quartile.We can use the Interquartile Range Calculator to help us calculate the interquartile range: We can use the following steps to calculate the range: This represents the spread of the middle 50% of values. The interquartile range measures the difference between the first quartile (25th percentile) and third quartile (75th percentile) in a dataset. The range measures the difference between the minimum value and the maximum value in a dataset. This tells us that the spread of the middle 50% of values is largest for dataset 2 and smallest for dataset 3.In statistics, the range and interquartile range are two ways to measure the spread of values in a dataset. The interquartile range can also be used to compare the spread of values between different datasets.įor example, suppose we have three datasets with the following IQR values: Comparing Interquartile Ranges Between Datasets Notice that the interquartile range barely changes when an outlier is present, while the standard deviation and range both dramatically change. We could use a calculator to find the following measures of spread for this dataset: This dataset has the following measures of spread To illustrate this, consider the following dataset: The benefit of using the interquartile range (IQR) to measure the spread of values in a dataset is that it is not affected by extreme outliers.įor example, an extremely small or extremely large value in a dataset will not affect the calculation of the IQR because the IQR only uses the values at the 25th percentile and 75th percentile of the dataset. Standard Deviation: Measures the typical deviation of individual values from the mean value in a dataset.Range: Measures the difference between the minimum and maximum value in a dataset.The interquartile range is one way to measure the spread of values in a dataset, but there are other measures of spread such as: This tells us that the middle 50% of values in the dataset have a spread of 14.5 inches. In simple terms, it measures the spread of the middle 50% of values.įor example, suppose we have the following dataset that shows the height of 17 different plants (in inches) in a lab:ĭataset: 1, 4, 8, 11, 13, 17, 19, 19, 20, 23, 24, 24, 25, 28, 29, 31, 32Īccording to the Interquartile Range Calculator, the interquartile range (IQR) for this dataset is calculated as: The interquartile range of a dataset, often abbreviated IQR, is the difference between the first quartile (the 25th percentile) and the third quartile (the 75th percentile) of the dataset.
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