Five Number Summary Calculator
Five Number Summary Calculator
Calculate Min, Q1, Median, Q3, Max & Create Box Plots
Separate values with commas, spaces, or line breaks
What is a Five Number Summary?
A five-number summary is a set of descriptive statistics that provides a comprehensive overview of the distribution of a dataset. These five key statistics give you a complete picture of your data's spread and central tendency.
Minimum Value
The smallest value in your dataset. This represents the lower bound of your data distribution and helps identify the starting point of your data range.
First Quartile (Q1)
The 25th percentile of your data. This value separates the lowest 25% of observations from the rest. It marks the boundary of the lower quarter of your dataset.
Median (Q2)
The middle value when data is sorted. Half of the observations fall below this value and half above. It's the 50th percentile and represents the center of your distribution.
Third Quartile (Q3)
The 75th percentile of your data. This value separates the highest 25% of observations from the rest. It marks the boundary of the upper quarter of your dataset.
Maximum Value
The largest value in your dataset. This represents the upper bound of your data distribution and helps identify the end point of your data range.
Box-and-Whisker Plot
A visual representation of the five-number summary. The box shows the interquartile range (IQR) from Q1 to Q3, with a line at the median. Whiskers extend to the minimum and maximum values (or to the fences if outliers exist).
Key Formulas
Interquartile Range (IQR)
Measures the spread of the middle 50% of data
Lower Fence
Values below this are considered outliers
Upper Fence
Values above this are considered outliers
Range
Total spread of the dataset
When to Use Five Number Summary?
- Quick Data Overview: Get a rapid understanding of your data's distribution without complex calculations
- Comparing Datasets: Easily compare different groups or datasets side by side
- Identifying Outliers: Spot unusual values that fall outside the typical range
- Skewness Detection: Understand if your data is symmetric or skewed left/right
- Statistical Reporting: Present data summaries in research papers and reports
- Quality Control: Monitor process variations in manufacturing and business
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