A national outdoor-equipment retailer maintains a loyalty database with about 2 million active customers distributed across four sales regions (North, South, East, and West). An analyst must email a satisfaction survey to 10 000 customers, and leadership insists that the results allow fair, statistically valid comparisons among the regions. After calculating how many customers belong to each region, the analyst also wants every selected customer to have an equal probability of being chosen. Which sampling technique best satisfies these requirements?
Stratified random sampling first divides the population into mutually exclusive subgroups-in this case, the four sales regions-then draws a separate random sample from each stratum, often in proportion to its size. This guarantees that every region is represented and that each selected customer still has an equal chance of inclusion within their region. Simple random sampling would treat the database as one undivided list and might under- or over-represent some regions by chance. Cluster sampling would select entire groups (for example, whole regions or sets of stores) rather than proportional, individual customers, so it would not automatically preserve the desired regional proportions. Convenience sampling relies on ease of access and provides neither random selection nor regional representation, so it clearly fails to meet the stated business and statistical goals.
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