Published on May 20, 2022 by Shaun Turney. Revised on June 17, 2022. Quartiles are three values that split sorted data into four parts, each with an equal number of observations. Quartiles are a type of
quantile. Quartiles can also split probability distributions into four parts, each with an equal probability. What are quartiles?Quartiles are a set of descriptive statistics. They summarize the central tendency and variability of a dataset or distribution. Quartiles are a type of percentile. A percentile is a value with a certain percentage of the data falling below it. In general terms, k% of the data falls below the kth percentile.
By splitting the data at the 25th, 50th, and 75th percentiles, the quartiles divide the data into four equal parts.
How to find quartilesTo find the quartiles of a dataset or sample, follow the step-by-step guide below.
There are multiple methods to calculate the first and third quartiles, and they don’t always give the same answers. There’s no universal agreement on the best way to calculate quartiles. Receive feedback on language, structure and formattingProfessional editors proofread and edit your paper by focusing on:
See an example Step-by-step exampleImagine you conducted a small study on language development in children 1–6 years old. You’re writing a paper about the study and you want to report the quartiles of the children’s ages.
1, 1, 2, 2, 2, 3, 3, 3, 3, 4, 5, 5, 6, 6 Step 3: Find the first quartilen * (1 / 4) = 14 * (1 / 4) = 3.53.5 is not an integer, so Q1 is the number at position 4. 1, 1, 2, 2, 2, 3, 3, 3, 3, 4, 5, 5, 6, 6 Q1 = 2 yearsStep 4: Find the second quartilen * (2 / 4) = 14 * (2 / 4) = 7 7 is an integer, so Q2 is the mean of the numbers at positions 7 and 8. 1, 1, 2, 2, 2, 3, 3, 3, 3, 4, 5, 5, 6, 6 Q2 = (3 + 3) / 2 Q2 = 3 yearsStep 5: Find the third quartilen * (3 / 4) = 14 * (3 / 4) = 10.5 10.5 is not an integer, so Q3 is the number at position 11. 1, 1, 2, 2, 2, 3, 3, 3, 3, 4, 5, 5, 6, 6 Q3 = 5 years Visualizing quartiles with boxplotsBoxplots are helpful visual summaries of a dataset. They’re composed of boxes, which show the quartiles, and whiskers, which show the lowest and highest observations. To make a boxplot, you first need to calculate the five-number summary:
With these five numbers, you can draw a boxplot: This isn’t the only method of drawing boxplots. Although the box always indicates the quartiles, often the whiskers indicate 1.5 IQR from the Q1 and Q3. 1, 1, 2, 2, 2, 3, 3, 3, 3, 4, 5, 5, 6, 6 To create a boxplot, the first step is to calculate the five-number summary:
These numbers determine the position of the box and whiskers on a number line: Interpreting quartilesQuartiles can give you useful information about an observation or a dataset. Comparing observationsQuartiles are helpful for understanding an observation in the context of the rest of a sample or population. By comparing the observation to the quartiles, you can determine whether the observation is in the bottom 25%, middle 50%, or top 25%. MedianThe second quartile, better known as the median, is a measure of central tendency. This middle number is a good measure of the average or most central value of the data, especially for skewed distributions or distributions with outliers. Interquartile rangeThe distance between the first and third quartiles—the interquartile range (IQR)—is a measure of variability. It indicates the spread of the middle 50% of the data. IQR = Q3 − Q1The IQR is an especially good measure of variability for skewed distributions or distributions with outliers. IQR only includes the middle 50% of the data, so, unlike the range, the IQR isn’t affected by extreme values. SkewnessThe distance between quartiles can give you a hint about whether a distribution is skewed or symmetrical. It’s easiest to use a boxplot to look at the distances between quartiles: Note that a histogram or skewness measure will give you a more reliable indication of skewness. Identifying outliersThe interquartile range (IQR) can be used to identify outliers. Outliers are observations that are extremely high or low. One definition of an outlier is any observation that is more than 1.5 IQR away from the first or third quartile. What are quantiles?A quartile is a type of quantile. Quantiles are values that split sorted data or a probability distribution into equal parts. In general terms, a q-quantile divides sorted data into q parts. The most commonly used quantiles have special names:
There is always one fewer quantile than there are parts created by the quantiles. How to find quantilesTo find a q-quantile, you can follow a similar method to that used for quartiles, except in steps 3–5, multiply n by multiples of 1/q instead of 1/4. For example, to find the third 5-quantile:
Practice questionsFrequently asked questions about quartiles and quantilesHow do I find quartiles in Excel? You can use the QUARTILE() function to find quartiles in Excel. If your data is in column A, then click any blank cell and type “=QUARTILE(A:A,1)” for the first quartile, “=QUARTILE(A:A,2)” for the second quartile, and “=QUARTILE(A:A,3)” for the third quartile. How do I find quartiles in R? You can use the quantile() function to find quartiles in R. If your data is called “data”, then “quantile(data, prob=c(.25,.5,.75), type=1)” will return the three quartiles. What is variability? Variability tells you how far apart points lie from each other and from the center of a distribution or a data set. Variability is also referred to as spread, scatter or dispersion. Cite this Scribbr articleIf you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.
Is this article helpful?You have already voted. Thanks :-) Your vote is saved :-) Processing your vote... Which measure of central tendency lies at the 50th percentile for any distribution *?The 50th percentile is used to indicate the middle point of the data set, which is the median.
What can be concluded by data that has a relatively low standard deviation?A standard deviation (or σ) is a measure of how dispersed the data is in relation to the mean. Low standard deviation means data are clustered around the mean, and high standard deviation indicates data are more spread out.
What do we call the tendency to exaggerate the correctness or accuracy?The tendency to exaggerate the correctness or accuracy of our beliefs and predictions is called. hindsight bias.
Why is an operational definition necessary?Your operational definitions describe the variables you will use as indicators and the procedures you will use to observe or measure them. You need an operational definition because you can't measure anything without one, no matter how good your conceptual definition might be.
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