Every element in the population has an equal chance of being selected. It's like a fair lottery draw, ensuring unbiased representation and making it a fundamental method in statistical research.
Stratified sampling involves dividing the population into subgroups or strata based on certain characteristics. Samples are then randomly selected from each stratum, allowing for a more precise analysis of each subgroup.
Systematic sampling involves selecting every kth item from a list after choosing a random starting point.It is easy to implement, making it practical for large populations with a systematic order.
Cluster Sampling
Dividing the population into clusters and then randomly selecting entire clusters for analysis.It is useful when it's impractical to sample individuals separately, providing a cost-effective approach.