Sampling Methods: Pros and Cons

Simple Random Sampling (SRS)

Every member has an equal chance of selection.

Bias Minimization

Reduces selection bias in sampling processes.

Implementation Ease

Utilizes random number generators for selection.

Subgroup Representation

May fail to represent all population subgroups.

Time Consumption

Can be difficult with large populations.

Stratified Random Sampling

Ensures proportional representation of subgroups.

Accurate Data

Provides better data for specific subgroups.

Knowledge Requirement

Needs detailed population knowledge for stratification.

Complexity

Implementation can be complex with many subgroups.

Cluster Sampling

Cost-effective for geographically dispersed populations.

Population List Availability

Useful when complete population list is missing.

Cluster Homogeneity

Bias risk if clusters are not homogenous.

Systematic Sampling

Simple method for large population lists.

Efficiency

More efficient than SRS in certain situations.

Hidden Patterns

May introduce bias if patterns exist in lists.