Understanding Interquartile Range: Examples and Applications

Table of contents
  1. Calculating the Interquartile Range
  2. Applications of Interquartile Range
  3. Frequently Asked Questions
  4. Conclusion

In statistics, the interquartile range (IQR) is a measure of statistical dispersion, or in simple terms, it describes how spread out the values in a dataset are. It is particularly useful when dealing with skewed data or outliers, as it focuses on the middle 50% of the data, effectively ignoring the extreme values at both ends. Understanding the interquartile range can provide valuable insights into the variability of a dataset, and it is a crucial concept in descriptive statistics. Let's explore some examples and applications of the interquartile range to gain a deeper understanding of its significance.

In this article, we will dive into the concept of the interquartile range, discuss its calculation, and explore practical examples to illustrate its relevance in real-world scenarios. We will also address frequently asked questions to provide comprehensive insights into this fundamental statistical measure.

Calculating the Interquartile Range

To understand the interquartile range, we first need to grasp the concept of quartiles. In a dataset, quartiles are the values that divide the data into four equal parts, each containing 25% of the data. The first quartile (Q1) represents the 25th percentile, the median (Q2) represents the 50th percentile, and the third quartile (Q3) represents the 75th percentile. The interquartile range is then calculated as the the difference between the third quartile (Q3) and the first quartile (Q1), or IQR = Q3 - Q1.

Example 1: Exam Scores

Let's consider a simple example to understand the calculation of the interquartile range. Suppose we have a dataset representing the scores of 20 students in a mathematics exam. The scores are as follows:

  • 60, 65, 70, 72, 75, 78, 80, 82, 85, 88, 90, 92, 95, 98, 100, 105, 110, 115, 120, 125

To calculate the interquartile range for this dataset, we first need to arrange the data in ascending order:

  1. 60, 65, 70, 72, 75, 78, 80, 82, 85, 88, 90, 92, 95, 98, 100, 105, 110, 115, 120, 125

Next, we identify the first quartile (Q1) and the third quartile (Q3) of the dataset. In this case, with 20 data points, Q1 will be the median of the first half of the data (10 data points) and Q3 will be the median of the second half of the data (also 10 data points).

Q1 = 77.5 and Q3 = 102.5

Finally, we calculate the interquartile range:

IQR = Q3 - Q1 = 102.5 - 77.5 = 25

Therefore, the interquartile range for the exam scores is 25, indicating that the middle 50% of the scores are spread over a range of 25 points.

Applications of Interquartile Range

The interquartile range is a valuable statistical measure with numerous applications in various fields. Let's explore some practical examples of how the IQR is used:

Example 2: Real Estate Prices

In the real estate industry, the interquartile range is used to analyze property prices within a specific area. By calculating the IQR of housing prices, analysts and real estate professionals can gain insights into the middle 50% of property values, which is particularly useful for determining the pricing trends and identifying potential outliers that may skew the data. This information can be instrumental for both buyers and sellers in understanding the market dynamics and making informed decisions.

Example 3: Stock Market Volatility

Financial analysts often use the interquartile range to measure the volatility of stock prices. By focusing on the middle 50% of price movements, the IQR provides a more robust measure of dispersion in the stock's performance, especially when compared to the full range of price fluctuations. This allows investors and traders to assess the stability and consistency of a stock's price movements, enabling them to make more strategic investment decisions.

Example 4: Health Data Analysis

In the field of healthcare, the interquartile range is applied to analyze patient data, such as blood pressure readings, cholesterol levels, or body mass index (BMI). By calculating the IQR for these metrics, healthcare professionals can identify the central tendency and variability within the middle 50% of the patient population, aiding in the assessment of health risks, the effectiveness of treatments, and the evaluation of overall wellness trends.

Frequently Asked Questions

What does the interquartile range tell us about the data?

The interquartile range provides insight into the spread or variability of the middle 50% of the data, effectively ignoring the extreme values at both ends. It gives a robust measure of dispersion, making it particularly useful in identifying the central tendency and understanding the distribution of values within the dataset.

How is the interquartile range different from the range?

While the range represents the difference between the maximum and minimum values in a dataset, the interquartile range focuses on the middle 50% of the data, providing a more resistant measure of variability that is less affected by extreme values or outliers.

Can the interquartile range be negative?

No, the interquartile range cannot be negative, as it represents the difference between the third quartile (Q3) and the first quartile (Q1). Since Q3 is always greater than or equal to Q1, the interquartile range will always be a non-negative value.


The interquartile range is an essential concept in statistics that offers a robust measure of spread, particularly for datasets with skewed distribution or potential outliers. By focusing on the middle 50% of the data, the IQR provides insights into the variability and central tendency, making it a valuable tool in descriptive statistics and various fields such as finance, healthcare, and real estate. Understanding the interquartile range empowers analysts, researchers, and decision-makers to extract meaningful information from datasets and make informed judgments based on the distribution of values within the middle 50%.

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