A data analyst for a retail chain is tasked with evaluating the effectiveness of a new marketing campaign. The campaign was piloted in a random sample of 50 out of the company's 750 stores. The analyst has sales data from the sample stores for the period before and during the campaign. The goal is to determine if the observed sales lift in the sample is statistically significant and can be expected to apply to the entire network of stores. Which statistical method should the analyst primarily use to make this determination?
The correct answer is inferential statistics. Inferential statistics are used to make generalizations or draw conclusions about a larger population based on data from a smaller sample. In this scenario, the analyst is using data from a sample of 50 stores to infer whether the marketing campaign will be effective across the entire population of 750 stores. This often involves techniques like hypothesis testing to determine if the observed effect is statistically significant.
Descriptive statistics would be used to summarize the data from the 50-store sample (e.g., calculating the average sales increase) but cannot be used on its own to generalize the findings to all stores.
Predictive statistics would be used to forecast future sales figures, which is a different goal than determining if the sample results are generalizable.
Prescriptive statistics would recommend a specific course of action (e.g., 'the company should roll out the campaign'), which is the business outcome that follows the analysis, not the statistical method used to test the sample's validity.
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