Identify statistical outliers in your data using the Interquartile Range (IQR) method. Calculate Q1, Q3, IQR, lower and upper fences, and see which values are potential outliers.
Enter your values and instantly analyze the data set.
An outlier is a data value that is unusually far away from the rest of the observations in a data set. Outliers can occur because of natural variation, measurement errors, unusual events, or other factors.
Statistical methods can help identify values that deserve additional attention. One of the most widely used methods is the Interquartile Range, or IQR, method.
This calculator uses the 1.5 × IQR rule to identify potential outliers. It first sorts the data, calculates Q1 and Q3, determines the IQR, and then establishes the lower and upper fences.
Suppose a data set contains the following values:
After sorting the values, Q1 and Q3 can be calculated. The IQR is then obtained by subtracting Q1 from Q3.
The lower and upper fences are calculated using the 1.5 × IQR rule. The value 100 may fall above the upper fence and therefore be classified as a potential outlier.
Outliers can have a significant effect on statistical analysis. For example, an unusually large value can increase the mean and make the data appear more spread out than most observations suggest.
Finding unusual observations helps researchers understand the distribution and quality of their data.
In manufacturing and business analysis, unusual values can sometimes indicate errors or processes that need further investigation.
Researchers may examine outliers before deciding how to analyze a data set. An outlier should not automatically be deleted; its cause should be investigated first.