🎯 What is Sampling?
Sampling is the process of selecting a subset (sample) from a larger group (population) to make inferences about the whole.
🧪 Two Main Categories:
1. Probability Sampling – Everyone has a known chance of being selected
| Technique | Description | Example |
|---|---|---|
| Simple Random | Every individual has an equal chance | Randomly picking 10 students from a list |
| Systematic | Every kth individual is selected | Selecting every 5th person in a queue |
| Stratified | Divide population into groups (strata), then sample | Sampling 10 students from each grade level |
| Cluster | Randomly choose entire groups (clusters) | Randomly pick 3 classrooms and survey all |
2. Non-Probability Sampling – Not every individual has a known chance
| Technique | Description | Example |
|---|---|---|
| Convenience | Select whoever is easiest to reach | Asking friends around you for feedback |
| Judgmental/Purposive | Select based on expert judgment | Choosing "top performers" for a case study |
| Quota | Like stratified, but not randomly selected | Surveying until each subgroup has 20 people |
| Snowball | Existing subjects refer more subjects | Used in hidden populations like drug users |
🧠 Why Sampling Matters:
- Saves time and resources
- Enables inference about a population
- Must be done carefully to avoid bias
🧪 Real-World Example:
A company wants feedback from 1,000 customers:
- Simple random: Randomly pick 1,000 from their database.
- Stratified: Divide customers by region and pick samples from each.
- Convenience: Ask whoever is online at the moment.
Would you like a quick diagram or visual flowchart to compare these methods?