How to Find 1st Quartile
In the world of statistics, quartiles are essential for understanding the distribution of data. They break down a dataset into four equal parts, with the first quartile (Q1) representing the 25th percentile, the second quartile (Q2) representing the median, and the third quartile (Q3) representing the 75th percentile. This guide will focus on helping you understand and calculate the first quartile in various scenarios.
Understanding Quartiles
Before diving into the calculation process, it is crucial to understand what quartiles are and why they are important in statistics. Quartiles are measures of central tendency that divide a dataset into four equal parts. They help in understanding the data distribution and identifying outliers.

The Importance of Quartiles in Data Analysis
Quartiles are useful in various ways, including:
 Providing a clear picture of data distribution
 Identifying the spread and skewness of the data
 Detecting potential outliers and extreme values
 Facilitating comparisons between different datasets

Terminology Related to Quartiles
 Quartiles: Q1, Q2, and Q3 represent the first, second, and third quartiles, respectively.
 Interquartile Range (IQR): The difference between the third and first quartiles (Q3 – Q1). It indicates the spread of the central 50% of the data.
 Outliers: Data points that lie significantly outside the IQR are considered outliers.
Finding the First Quartile (Q1)
There are different methods to calculate the first quartile, depending on the dataset and the context. Here, we will discuss two main methods for finding Q1: manual calculation and using software.

Manual Calculation
a. Organize the Data
Start by sorting your data in ascending order. For example, consider the following dataset:
23, 12, 45, 34, 56, 21, 29, 18
Sort the data:
12, 18, 21, 23, 29, 34, 45, 56
b. Determine the Position of Q1
To find the position of Q1, use the following formula:
Position of Q1 = (n + 1) / 4
Where “n” is the number of data points. In our example, n = 8.
Position of Q1 = (8 + 1) / 4 = 9 / 4 = 2.25
Since the position is not a whole number, Q1 lies between the 2nd and 3rd data points.
c. Calculate Q1
Interpolate Q1 using the two data points surrounding the position:
Q1 = Data point at 2nd position + (0.25 x Difference between the 2nd and 3rd data points)
Q1 = 18 + (0.25 x (21 – 18))
Q1 = 18 + (0.25 x 3) = 18 + 0.75 = 18.75

Using Software and Tools
Various statistical software and tools, such as Excel, R, Python, and SPSS, can help you calculate Q1 quickly and efficiently.
a. Excel
In Excel, you can use the QUARTILE function:
=QUARTILE(range, quart)
For our dataset, the formula would be:
=QUARTILE(A1:A8, 1)
b. R
In R, use the quantile function:
quantile(data, probs = 0.25, na.rm = FALSE)
c. Python
In Python, you can use the numpy library:
import numpy as np
data= [12, 18, 21, 23, 29, 34, 45, 56] Q1 = np.percentile(data, 25)
d. SPSS
In SPSS, you can find the first quartile through the “Frequencies” procedure, under the “Descriptive Statistics” option. Select your variable, and then check the box for “Quartiles.”
Common Misconceptions and Mistakes

Confusing Q1 with the Median
Remember that Q1 is the first quartile, representing the 25th percentile, while the median (Q2) is the second quartile, representing the 50th percentile.

Ignoring Outliers
When calculating Q1, it’s essential to consider any potential outliers as they can significantly affect the results.

Not Sorting the Data
Always sort the data in ascending order before attempting to calculate Q1 or any other quartile.
Frequently Asked Questions
What is the first quartile (Q1)?
The first quartile, or Q1, represents the value below which 25% of the data points in a dataset fall. It is the 25th percentile of the data.
How do I find the first quartile?
To find Q1, first, sort the data in ascending order. Then, use the formula (n + 1) / 4 to find the position of Q1. If the position is not a whole number, interpolate Q1 using the two data points surrounding the position.
What is the difference between Q1 and the median?
Q1 represents the 25th percentile of the data, while the median (Q2) represents the 50th percentile.
How are quartiles used in data analysis?
Quartiles are used to understand the distribution of data, identify the spread and skewness, detect potential outliers, and facilitate comparisons between different datasets.
Can I calculate Q1 using software?
Yes, you can calculate Q1 using various statistical software and tools, such as Excel, R, Python, and SPSS.
What is the interquartile range (IQR)?
The IQR is the difference between the third quartile (Q3) and the first quartile (Q1). It represents the spread of the central 50% of the data.
How do I identify outliers using quartiles?
Outliers are data points that lie significantly outside the interquartile range (IQR). A common method to detect outliers is to use 1.5 times the IQR below Q1 or above Q3 as the threshold.
Can I calculate quartiles for categorical data?
No, quartiles can only be calculated for numerical (quantitative) data.
What is the relationship between quartiles and percentiles?
Quartiles are a type of percentile. Specifically, Q1 is the 25th percentile, Q2 is the 50th percentile (or median), and Q3 is the 75th percentile.
Are there other ways to divide data besides quartiles?
Yes, data can also be divided into other segments, such as deciles (which divide the data into 10 equal parts) or percentiles (which divide the data into 100 equal parts).