Simple random sampling involves selecting a sample from a population in such a way that every member of the population has an equal chance of being chosen.

Stratified sampling ensures that different subgroups within a population are adequately represented in the sample, leading to greater representativeness.

Convenience sampling involves selecting a sample based on the researcher's convenience or accessibility, without regard to the probability of selection.

Systematic sampling can introduce bias if there is a pattern in the population that coincides with the sampling interval.

Cluster sampling involves dividing the population into clusters and then randomly selecting a sample of clusters. The members of the selected clusters are then included in the sample.

Quota sampling is relatively inexpensive to conduct, as it does not require a complete list of the population.

Purposive sampling involves selecting a sample based on the researcher's knowledge of the population, with the aim of obtaining information from specific individuals or groups.

Convenience sampling can introduce bias, as the sample may not be representative of the entire population.

Quota sampling involves selecting a sample based on a predetermined quota for different subgroups within the population, ensuring that each subgroup is adequately represented.

Stratified sampling provides increased precision in estimating population parameters compared to simple random sampling, as it ensures that different subgroups within the population are adequately represented.

Systematic sampling involves selecting a sample based on a random starting point and then selecting every $k$th member of the population.

Cluster sampling can introduce bias if the clusters are not representative of the entire population.

Purposive sampling involves selecting a sample based on the researcher's judgment of who would be most informative, with the aim of obtaining in-depth information from specific individuals or groups.

Convenience sampling is easy to implement, as it does not require a complete list of the population or a complex sampling design.

Systematic sampling involves selecting a sample based on a random starting point and then selecting every $k$th member of the population.