reply to Discussion

1. Compare and Contrast: Simple Random Sampling vs. Stratified Sampling

With simple random sampling, each member of the population has an equal probability of being chosen. This strategy is statistically robust, lessens selection bias, and straightforward to apply (Clarkson et al., 2021). It could not be effective, though, if there are distinct subgroups among the population. The data, for example, may miss important information like geography or age if a business randomly selects a sample of its consumers without taking such factors into account.

In contrast, stratified sampling separates the population into meaningful subgroups or strata, such as localities, age groups, or departments, and chooses random samples from each stratum. This method can produce more accurate findings and improve representation across important criteria (Clarkson et al., 2021). When population variety might affect the study findings, such as when examining customer input from various locations or demographics, stratified sampling is frequently chosen in a corporate setting.