Quartile Calculator

Paste in a data set and get the first, second, and third quartiles, the interquartile range, and a handful of other basic stats in one shot. Try our Percentage Decrease Calculator, Percentage Difference Calculator, or Quadratic Formula Calculator.

Enter Your Data Set

Separate values with commas, spaces, or new lines you can even mix delimiters, and it'll still parse correctly. Pasting a column straight out of a spreadsheet works too.

Accepted Formats

Format Example
Comma-separated12, 18, 25, 30
Space-separated12 18 25 30
One per line12
18
25
30
Mixed12, 18 25,,30

Quartile Statistics

First Quartile, Q1
Second Quartile, Q2 (Median)
Third Quartile, Q3
Interquartile Range, IQR
Median
Minimum
Maximum
Range
Values entered

Share this calculation:

Enter a data set and hit Calculate to see the quartiles here.

How to Use This Calculator

  1. Paste or type your data set into the box commas, spaces, or line breaks all work, and you don't need to sort the values first.
  2. Click Calculate to get Q1, Q2, Q3, the interquartile range, and the min/max/range in one pass.
  3. Copy the results as plain text or print a clean summary using the buttons under the table.
  4. Share the exact data set and result with a link if you need someone else to check your work.

Practical Example / Formula Used

Quartiles split an ordered data set into four equal chunks. Q1 marks the 25th percentile, Q2 the 50th (which is just the median), and Q3 the 75th. Finding them by hand follows a short recipe:

1. Sort the data.
2. Q2 = median of the whole set.
3. Q1 = median of the lower half (excluding Q2 if the count is odd).
4. Q3 = median of the upper half (excluding Q2 if the count is odd).
5. IQR = Q3 − Q1

Here's a worked example using {6, 7, 15, 36, 39, 40, 41, 42, 43, 47, 49} 11 values, already sorted. With an odd count, the median (40) sits right in the middle and gets left out of both halves. The lower half is {6, 7, 15, 36, 39}, whose median is 15 that's Q1. The upper half is {41, 42, 43, 47, 49}, whose median is 43 that's Q3. So IQR = 43 − 15 = 28.

When the data set has an even count instead, there's no single middle value to exclude, so the lower and upper halves are just a straight split down the middle, and Q2 becomes the average of the two central values.

Why Use This Calculator?

  • It handles messy pasted data - mixed commas, spaces, and line breaks all parse the same way, so you don't have to clean it up first.
  • You get min, max, and range alongside the quartiles, which is usually what you need anyway when summarizing a data set.
  • The odd-vs-even handling for excluding the median is done automatically, which is the step most people get wrong doing this by hand.
  • Share links make it simple to hand off a data set and result to a classmate or coworker.

Key Factors / Variables Explained

Q1, first quartile - the value below which the lowest 25% of the data falls.

Q2, second quartile - the median of the entire data set; 50% of values fall below it.

Q3, third quartile - the value below which the lowest 75% of the data falls.

IQR, interquartile range - the spread of the middle 50% of the data, calculated as Q3 minus Q1. It's a common way to measure spread that isn't thrown off by extreme outliers the way range is.

Range - the difference between the largest and smallest values in the set.

Frequently Asked Questions

It's to keep the lower and upper halves equal in size. If you kept the median in both halves, you'd be counting that middle value twice, which skews Q1 and Q3 slightly toward the center. Leaving it out of both halves keeps things symmetric.

Yes, there are actually several accepted methods, and they can give slightly different Q1/Q3 values for the same data set, especially with small samples. This calculator uses the exclusive median method described above, which is one of the most commonly taught versions in intro statistics courses.

Range only looks at the two extreme values, so one unusually high or low outlier can throw it off completely. IQR ignores the outer 25% on each end, which makes it a steadier way to describe how spread out the "typical" values are.

You need at least two values for this to mean anything, though quartiles get more meaningful with more data. With only a handful of points, Q1 and Q3 can end up equal to individual values in your set rather than genuinely representing a quarter of the distribution.

Yes, that's actually one of the most common uses. A widely used rule of thumb flags any value more than 1.5 times the IQR below Q1 or above Q3 as a potential outlier worth a closer look.

Explore our advanced utility parameters and chronological tracking tools: