Data Collection and Sampling Techniques
Statistics is only as good as the data it is based on. If data is collected poorly, even the best analysis will lead to wrong conclusions.
This lesson focuses on how data is collected and how samples are chosen from a population in a reliable way.
Population vs Sample
Before collecting data, it is important to understand two key terms:
- Population – The entire group we want to study
- Sample – A subset taken from the population
In most real-world situations, studying the entire population is not practical. That is why we rely on samples.
Real-World Example
If a company wants to know customer satisfaction:
- Population → All customers of the company
- Sample → A selected group of customers surveyed
Why Sampling Is Necessary
- Saves time
- Reduces cost
- Makes large studies feasible
- Allows faster decision-making
The key is to ensure the sample represents the population well.
Methods of Data Collection
Data can be collected using different methods depending on the goal of the study.
| Method | Description | Example |
|---|---|---|
| Surveys | Collect data through questionnaires | Customer feedback forms |
| Experiments | Controlled studies to test cause and effect | Drug testing in medicine |
| Observations | Recording behavior without interference | Traffic flow analysis |
| Existing Records | Using already available data | Government census data |
Sampling Techniques
Sampling techniques determine how individuals are selected from the population.
Simple Random Sampling
Every member of the population has an equal chance of being selected.
This method reduces bias and is easy to understand.
Example: Selecting 100 students randomly from a university list.
Systematic Sampling
Every kth member of the population is selected after a random start.
Example: Selecting every 10th customer entering a store.
Stratified Sampling
The population is divided into subgroups (strata), and samples are taken from each group.
Example: Sampling students separately from each academic year.
Cluster Sampling
The population is divided into clusters, and entire clusters are randomly selected.
Example: Selecting a few schools and surveying all students in them.
Comparison of Sampling Techniques
| Technique | Key Idea | When to Use |
|---|---|---|
| Simple Random | Equal chance for all | When population list is available |
| Systematic | Every kth item | When data is ordered |
| Stratified | Sample each subgroup | When subgroups matter |
| Cluster | Sample entire groups | When population is large and spread out |
Common Sampling Errors
- Biased samples
- Too small sample size
- Non-random selection
Good sampling aims to minimize these errors.
Quick Check
Why is stratified sampling useful?
Practice Quiz
Question 1:
Which sampling method gives every individual an equal chance?
Question 2:
Which sampling method is best when the population is geographically spread out?
Mini Practice
A university wants to study student satisfaction across departments.
- Which sampling technique would best ensure fair representation?
What’s Next
In the next lesson, we will study Data Bias and Common Errors, which explains what can go wrong even with large datasets.