Descriptive Statistics

Alagai Augusten avatar   
Alagai Augusten
Learn how to summarize and organize characteristics of a data set using descriptive statistics. Find out the types of descriptive statistics (distribution, central tendency, variability) and how to ca..

Definition: Descriptive statistics are numerical measures that summarize and describe the main features of a dataset.

Purpose: They help us understand the characteristics of data without making any inferences beyond the dataset itself.

Importance: Descriptive statistics provide valuable insights into central tendencies, variability, and distribution of data.

Descriptive statistics provide a summary of the data and are useful for understanding its characteristics before further analysis or interpretation. They are often used in various fields such as economics, psychology, sociology, and natural sciences to describe and analyze data.

Here are some common descriptive statistics;

Measures of Central Tendency:

Measures of central tendency are statistical measures that summarize the center or midpoint of a dataset. They provide insight into the typical or central value around which the data points tend to cluster. Here we explain the main measures of central tendency:


The mean, also known as the average, is the sum of all values in the dataset divided by the number of observations.

It is sensitive to outliers, as extreme values can disproportionately influence the mean.


The median is the middle value of a dataset when the values are arranged in ascending or descending order. It is not affected by extreme values and is therefore more robust to outliers compared to the mean. If the dataset has an odd number of observations, the median is the middle value. If the dataset has an even number of observations, the median is the average of the two middle values. The median is particularly useful when the data is skewed or not normally distributed.


The mode is the value that occurs most frequently in the dataset.

A dataset may have one mode (unimodal), two modes (bimodal), or more than two modes (multimodal). Unlike the mean and median, the mode can be used for both numerical and categorical data. It is useful for identifying the most common or popular value in a dataset.

Understanding the appropriate measure of central tendency to use depends on the distribution and characteristics of the dataset. In practice, it's often valuable to consider all three measures together to gain a comprehensive understanding of the data's central tendency.

Measures of Variability

Measures of variability, also known as measures of dispersion, quantify the spread or dispersion of data points in a dataset. They provide insight into how spread out the values are from the central tendency measures such as the mean or median. The main measures of variability are as bellow:


The range is the simplest measure of variability and is calculated as the difference between the maximum and minimum values in the dataset. It provides a basic understanding of the spread of the data but is sensitive to outliers.



Variance measures the average squared deviation of each data point from the mean. It provides a more precise measure of variability compared to the range.

Standard Deviation:

The standard deviation is the square root of the variance and is expressed in the same units as the original data. It provides a more interpretable measure of variability compared to the variance.

Interquartile Range (IQR):

The interquartile range is the difference between the third quartile (Q3) and the first quartile (Q1) and represents the middle 50% of the data. It is less sensitive to outliers compared to the range and standard deviation.

Understanding the variability of data is essential for making informed decisions and drawing accurate conclusions from statistical analyses. Different measures of variability may be more appropriate depending on the nature of the data and the specific research questions being addressed.

Measures of Distribution

Measures of distribution, also known as measures of shape, describe the characteristics of the distribution of data points in a dataset. They help us understand the pattern, symmetry, and spread of the data. Bellow are the main measures of distribution:


Skewness measures the asymmetry of the distribution.

A distribution is symmetric if it has zero skewness.

Positive skewness indicates a distribution with a tail extending towards higher values, while negative skewness indicates a tail extending towards lower values.

Skewness is calculated using the third standardized moment.


Kurtosis measures the peakedness or flatness of the distribution.

A normal distribution has a kurtosis of 3 (mesokurtic).

Leptokurtic distributions have a higher peak and heavier tails than the normal distribution (kurtosis > 3).

Platykurtic distributions have a lower peak and lighter tails than the normal distribution (kurtosis < 3).

Kurtosis is calculated using the fourth standardized moment.

Measures of distribution are crucial for understanding the shape and characteristics of datasets, allowing for more informed analysis and decision-making in various fields.

Percentiles and Quartiles

Percentiles and quartiles are statistical measures that divide a dataset into equal parts, allowing for a better understanding of its distribution. Here's an explanation of each:


Percentiles divide a dataset into hundred equal parts, representing the percentage of data points that fall below a certain value.

For example, the 25th percentile (also known as the first quartile, Q1) indicates that 25% of the data points fall below this value.

Percentiles are often used in standardized testing, where scores are compared to the performance of other test-takers.


Quartiles divide a dataset into four equal parts.

The first quartile (Q1) is the value below which 25% of the data points fall.

The second quartile (Q2), also known as the median, is the value below which 50% of the data points fall.

The third quartile (Q3) is the value below which 75% of the data points fall.

Quartiles are commonly used in descriptive statistics and box plots to understand the spread and central tendency of the data.

In summary, percentiles and quartiles provide valuable insights into the distribution of data, helping analysts and researchers make informed decisions and draw meaningful conclusions.

Frequency Distribution

Frequency distribution is a statistical representation of the frequency or count of each distinct value or category in a dataset. It provides a summary of the distribution of values, showing how often each value occurs. Here's an overview of frequency distribution:

Frequency Table:

A frequency table is a tabular representation of the counts of different values or categories in a dataset. It consists of two columns: one for the values or categories and another for their corresponding frequencies. The values/categories are listed in one column, and their respective frequencies are listed in the adjacent column.

Example: Consider a dataset of exam scores: {75, 80, 85, 90, 75, 85, 90, 85, 80, 75}.

The frequency table for this dataset would list each unique score along with the count of how many times it appears:

Score   Frequency

75      3

80      2

85      3

90      2


A histogram is a graphical representation of a frequency distribution.

It consists of bars where the height of each bar corresponds to the frequency of the corresponding value or category.

The bars are usually contiguous, with no gaps between them, representing the range of values.

Relative Frequency:

Relative frequency is the proportion of times a value or category occurs relative to the total number of observations in the dataset.

It is calculated by dividing the frequency of each value or category by the total number of observations.

Cumulative Frequency:

Cumulative frequency is the running total of frequencies as values or categories are added in ascending or descending order.

It helps analyze the number of observations below or above a certain value or category.

Frequency distribution provides a clear and concise summary of the distribution of values in a dataset, making it easier to understand and interpret data patterns and characteristics.


Graphical Representations

Graphical representations are visual tools used to present data in a clear and concise manner, making it easier to interpret and analyze. Lets take a look at some common graphical representations used in data analysis:


Histograms display the distribution of numerical data by dividing it into intervals, or bins, and plotting the frequency of data points within each interval as bars. They provide a visual representation of the shape, center, and spread of the data distribution.

Box Plots (Box-and-Whisker Plots):

Box plots summarize the distribution of numerical data using quartiles. They display the median (middle line within the box), quartiles (edges of the box), and any outliers (individual points beyond the whiskers). Box plots are useful for comparing the distribution of data across different groups or categories.

Scatter Plots:

Scatter plots display the relationship between two numerical variables by plotting each data point as a point on a Cartesian plane. They are useful for identifying patterns, trends, and correlations between variables.

Line Graphs:

Line graphs represent the relationship between two numerical variables over time or another continuous variable. They are particularly useful for showing trends and changes in data over time.

Bar Charts:

Bar charts represent categorical data by displaying the frequency or proportion of data points within each category as bars. They are useful for comparing the distribution of data across different categories.

Pie Charts:

Pie charts represent categorical data as slices of a circle, with each slice corresponding to the proportion of data points within a category. They are useful for illustrating the relative proportions of different categories within a dataset.


Heatmaps display numerical data in a matrix format, with colors representing the magnitude of each data point. They are useful for visualizing patterns and relationships in large datasets, such as correlation matrices or geographic data.

Q-Q Plots (Quantile-Quantile Plots):

Q-Q plots compare the distribution of a dataset to a theoretical distribution, such as the normal distribution. They are useful for assessing whether a dataset follows a particular distribution and identifying deviations from expected patterns.

Graphical representations provide intuitive ways to explore and understand data, allowing for better insights and communication of findings. Different types of graphs are chosen based on the nature of the data, the research questions being addressed, and the audience's preferences for visualization.

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