Descriptive vs Inferential Statistics
Statistics is broadly divided into two main branches based on what we want to do with data. Sometimes we only want to describe the data we have. Other times, we want to make conclusions about a larger group.
These two goals lead to two important types of statistics: Descriptive Statistics and Inferential Statistics.
Descriptive Statistics
Descriptive statistics focuses on summarizing and organizing data that has already been collected.
It does not make predictions or generalizations. It simply answers:
- What does the data look like?
- What is typical or average?
- How spread out is the data?
Common descriptive tools include averages, percentages, tables, and charts.
Real-World Example (Descriptive)
Suppose a teacher records the marks of students in a class:
70, 75, 80, 85, 90
Using descriptive statistics, the teacher may say:
- The average mark is 80
- The lowest mark is 70
- The highest mark is 90
Here, the teacher is only describing the data that already exists.
Inferential Statistics
Inferential statistics goes a step further. It uses sample data to make conclusions or predictions about a population.
Inferential statistics answers questions such as:
- What can we say about the entire population?
- Is a result likely due to chance?
- Can we make predictions with confidence?
Because it involves uncertainty, inferential statistics always includes probability and estimation.
Real-World Example (Inferential)
A company surveys 200 customers and finds that 70% are satisfied.
Using inferential statistics, the company concludes:
“Most customers are likely satisfied with our product.”
Here, the company is using a sample to make a conclusion about all customers.
Key Difference in Simple Words
Think of it this way:
- Descriptive statistics → Describe what you see
- Inferential statistics → Predict or conclude beyond what you see
Both are equally important and are often used together.
Numerical Example
Imagine a factory produces thousands of bulbs every day.
A quality engineer tests 50 bulbs and finds:
- Average lifetime = 1,000 hours
This average is a descriptive statistic for the sample.
When the engineer says:
“The average lifetime of all bulbs produced today is about 1,000 hours”
That statement is inferential.
When Do We Use Each?
We use descriptive statistics when:
- Exploring a dataset
- Summarizing results
- Presenting reports
We use inferential statistics when:
- Making predictions
- Testing assumptions
- Making decisions with incomplete data
Practice Questions
Question 1:
A report shows the average salary of employees in a company.
Is this descriptive or inferential?
Answer:
Descriptive. It summarizes known data.
Question 2:
A survey of 300 voters is used to predict the election result.
Is this descriptive or inferential?
Answer:
Inferential. A sample is used to predict a population outcome.
Question 3:
Listing the number of students in each grade of a school is an example of which type?
Answer:
Descriptive statistics.
Mini Practice (Think Before Answering)
A fitness app records the daily step count of 1,000 users and calculates the average.
- Is calculating the average descriptive?
- If the app claims all users walk 8,000 steps per day, is that inferential?
Explanation:
Calculating the average is descriptive. Making a claim about all users is inferential.
What’s Next
In the next lesson, we will begin Descriptive Statistics in detail by learning about Frequency Tables and Distributions.