Calculate quartiles (Q0, Q1, Q2, Q3, Q4), interquartile range, and analyze data distribution by percentiles.
Last updated: March 2026
Quartiles divide a sorted dataset into four equal parts. They show where the 25th, 50th, and 75th percentiles fall, giving you insight into data spread and distribution.
Q1 (25th percentile): 25% of data is below this value. Q2 (50th percentile/median): 50% of data is below this. Q3 (75th percentile): 75% of data is below this. The interquartile range (IQR) equals Q3 minus Q1, representing the middle 50% of values.
Quartiles help identify outliers, skewness, and data consistency. They are fundamental to box plots, which use Q0 (min), Q1, Q2, Q3, and Q4 (max) to visualize data distributions.
Dataset: 6, 7, 15, 36, 39, 40, 41, 42, 43, 47, 49
Q2 is the 50th percentile, also known as the median. Half the data falls below it, half above it.
The IQR (Q3 minus Q1) measures variability in the middle 50% of data and is crucial for identifying outliers in box plots.
Values below Q1 minus 1.5 times IQR or above Q3 plus 1.5 times IQR are typically considered statistical outliers.
Yes! If your dataset contains negative values, quartiles can be negative. Quartiles reflect the actual data distribution.
The median divides data in half (Q2). Quartiles divide it into four parts (Q0, Q1, Q2, Q3, Q4), showing more detail about distribution.
Technically you need at least 1 value, but quartiles are most meaningful with larger samples. We require at least 4 values.
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