Calculate Q1, Q2, Q3 and the interquartile range from any numerical data set. Get accurate quartile values with a clear step-by-step result.
Enter your data values to calculate Q1, Q2, Q3 and IQR.
Quartiles are statistical values that divide an ordered data set into four approximately equal parts. They are useful for understanding how values are distributed and how widely the data is spread.
The three main quartiles are Q1, Q2, and Q3. Q1 represents the 25th percentile, Q2 represents the median or 50th percentile, and Q3 represents the 75th percentile.
The calculator first sorts your values from smallest to largest. It then determines Q1, Q2, and Q3 using percentile-style linear interpolation. Finally, it subtracts Q1 from Q3 to calculate the interquartile range.
When using the linear interpolation method, the percentile position can be calculated using:
Here, P is the desired percentile and n is the number of values in the sorted data set.
Q1 is the 25th percentile and represents the point below which approximately 25% of the data falls.
Q2 is the 50th percentile and is also called the median.
Q3 is the 75th percentile and represents the point below which approximately 75% of the data falls.
The interquartile range measures the spread of the middle 50% of the data.
Consider this data set:
The values are already arranged from smallest to largest. Using the linear interpolation method:
Therefore, the interquartile range is:
Quartiles provide a useful summary of the distribution of a data set. Instead of looking only at the minimum and maximum values, quartiles show where the lower, middle, and upper portions of the data are located.
Q1, Q2, and Q3 can help identify whether values are concentrated in certain parts of a distribution.
The interquartile range is frequently used when identifying potential outliers. A common rule considers values below Q1 − 1.5 × IQR or above Q3 + 1.5 × IQR as potential outliers.
Quartiles can make it easier to compare distributions, especially when data sets contain extreme values that could strongly affect the mean.