Descriptive statistics involve techniques used to summarize, organize, and present data in a meaningful way. Instead of drawing conclusions or making predictions, descriptive statistics focus on describing the main characteristics of a dataset so that it becomes easier to understand and interpret.

Descriptive statistics typically address three key aspects of data:

Measures of Central Tendency

These describe the “center” or average value in the dataset.
Common measures include:

• Mean – the arithmetic average
• Median – the midpoint of the data
• Mode – the most frequently occurring value

These values provide a representative figure around which most data points cluster.

Measures of Variability (Dispersion)

These describe how spread out or dispersed the data are. Common measures include:

• Range – difference between the largest and smallest value
• Variance – average squared deviation from the mean
• Standard Deviation (SD) – the square root of variance, showing how much values typically differ from the mean

Variability tells us whether values are closely grouped or widely scattered.

Graphical Representations

Descriptive statistics also involve presenting data visually to highlight trends and patterns. Frequently used graphs include:

• Histograms
• Bar charts
• Pie charts
• Box-and-whisker plots

Graphs provide quick insight into the shape, spread, and central value of a distribution.

Purpose of Descriptive Statistics

The primary goal of descriptive statistics is to simplify complex data. By reducing large datasets into understandable summaries, descriptive statistics:

• Facilitate comparisons
• Support decision-making
• Help detect patterns and deviations

Unlike inferential statistics, descriptive statistics do not generalize findings beyond the dataset; they simply describe what the data show.