Descriptive Statistics Overview

Before performing advanced statistical tests, it is essential to understand the basic characteristics of data. Descriptive statistics provide a summary of data using numbers that describe central tendency, spread, and overall distribution.

Descriptive statistics do not make predictions or test hypotheses. Instead, they help you understand what the data looks like and how values are distributed.

Why Descriptive Statistics Matter

Every professional analysis begins with descriptive statistics. Without them, it is impossible to interpret advanced statistical results correctly.

Descriptive statistics help you:

• Understand the typical value in a dataset
• Measure variability and spread
• Identify unusual or extreme values
• Check data quality before analysis

They form the foundation for all further analysis in SPSS.

Main Components of Descriptive Statistics

Descriptive statistics are usually divided into two main categories.

Measures of Central Tendency describe where data values tend to cluster.

• Mean – average value
• Median – middle value
• Mode – most frequent value

Measures of Dispersion describe how spread out the data is.

• Range
• Variance
• Standard Deviation

Example Dataset

Consider the following dataset of exam scores:

Descriptive statistics help summarize this data into meaningful numbers.

Using SPSS to Generate Descriptive Statistics

SPSS provides built-in tools to calculate descriptive statistics easily.

Using the menu:

• Go to Analyze → Descriptive Statistics → Descriptives
• Select the variable
• Click OK

SPSS instantly produces summary output including mean and standard deviation.

Using SPSS Syntax

Syntax allows you to produce descriptive statistics quickly and consistently.

DESCRIPTIVES VARIABLES=Score
  /STATISTICS=MEAN STDDEV MIN MAX.

This command calculates mean, standard deviation, minimum, and maximum values.

Interpreting the Output

Understanding output is as important as generating it.

For example:

• The mean indicates overall performance
• The median shows the central value
• The standard deviation shows how varied the scores are

A high standard deviation indicates large differences between individuals, while a low value indicates consistency.

Common Beginner Mistakes

Some common mistakes when using descriptive statistics include:

• Ignoring outliers
• Using mean for highly skewed data
• Skipping descriptive analysis entirely

Descriptive statistics should always be the first analytical step.

Quiz 1

What is the main purpose of descriptive statistics?

Quiz 2

Which measure represents the average value?

Quiz 3

What does standard deviation measure?

Quiz 4

Why should descriptive statistics be done first?

Quiz 5

Which SPSS menu generates descriptive statistics?

Mini Practice

Create a dataset with a variable called Sales containing at least 10 numeric values.

Generate descriptive statistics and interpret the mean and standard deviation.

What’s Next

In the next lesson, you will work with frequencies, means, and crosstabs, which provide deeper insights into categorical and numeric data.