* Purposive Sampling: Involves selecting participants based on specific characteristics or criteria, rather than randomly. This does not apply to your scenario. * Snowball Sampling: Involves participants referring others to participate in the survey, often used in studies where participants are hard to reach. This does not apply here. Given the information, if customers are selected randomly without additional stratification, Random Sampling is the best fit. However, since random sampling is not listed among your choices, and Stratified Sampling is the closest in context if there was an implicit stratification, the answer would be: B. Stratified Sampling If the sample is selected without regard to subgroups, then none of the options perfectly fit, but the intention behind stratified sampling (if considering the importance of categorization before sampling) makes it the closest option.

Stratified Sampling not in the guide. Researched it and: The scenario described is an example of random sampling from a large pool of customers to ensure the accuracy of survey results. To match this with the options provided: * Quota Sampling: Involves selecting a specific number of participants from predefined categories or quotas. This is not described in your scenario. * Stratified Sampling: Involves dividing the population into distinct subgroups or strata (e.g., based on purchase categories) and then randomly sampling from each stratum. Although this method is related to your scenario, the excerpt does not specify that customers were divided into strata before sampling.

See 2.2.2 Determine the Data Sets... In this context, the guide provides foundational knowledge on how sampling is a key aspect of managing and analyzing large datasets. It ensures that the data used for analysis is both manageable and representative, aligning with the need to address data volume and variety efficiently. This excerpt confirms that the concepts related to sampling and its importance in data management are indeed covered in the section about determining data sets and handling large volumes of data.