1. Sampling

Definition: Sampling is the process of selecting a subset (sample) of individuals or items from a larger population to estimate characteristics of the whole population.

Purpose of Sampling:

• Saves time, cost, and effort
• Enables quick decision-making
• Provides reliable estimates if designed correctly

2. Key Terms

3. Sampling Design

Definition: A sampling design is a plan or strategy that specifies how to select a sample from a population.

Steps in Sampling Design:

1. Define the population clearly.
2. Decide the sample size based on accuracy, cost, and variability.
3. Choose the sampling method (probability or non-probability).
4. Select the sample according to the method.
5. Collect data from the selected sample.

Types of Sampling Designs

A. Probability Sampling (Random Selection)

Every element has a known, non-zero probability of being selected. Examples:

• Simple Random Sampling
• Stratified Sampling
• Systematic Sampling
• Cluster Sampling

B. Non-Probability Sampling (Non-Random Selection)

Selection is not based on probability. Examples:

• Convenience Sampling
• Judgmental Sampling
• Quota Sampling

Note: Probability sampling allows estimation of sampling error, whereas non-probability sampling does not.

4. Sampling Frame

Definition: A sampling frame is a list or representation of all the elements in the population from which the sample is drawn.

Importance:

• Ensures the sample represents the population
• Avoids coverage errors (missing or duplicated elements)

Example:

• Population: All students in a university
• Sampling frame: List of all registered students for the current semester

Key Point: A poor or incomplete sampling frame can lead to biased results, even if the sampling method is correct.

5. Summary Diagram (Conceptual)

Population (Universe)
     |
     v
Sampling Frame (List of elements)
     |
     v
Sampling Design (Plan)
     |
     v
Sample Selected
     |
     v
Data Collection & Analysis

6. Key Points to Remember

1. Population → Sampling frame → Sample → Analysis is the correct order.
2. A good sampling design ensures representativeness and minimizes bias.
3. Sampling frame quality is crucial; missing or duplicate elements reduce reliability.
4. Probability sampling → estimation with known error; Non-probability → cannot measure error accurately.