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When investigating scenarios like the department store example, it's crucial to distinguish between the population and the sample. The **population** refers to the entire group of interest, which, in this case, means all customers who purchase from the department store, both in-store and online.
Populations are often too large to study directly, so we use a **sample**, which is a smaller, manageable group selected from the population. In this example, the sample consists of customers approached in-store for their purchase volumes.
Capturing an accurate sample is key because it affects the validity of research conclusions. If the sample doesn't properly represent the entire population, the findings could be misleading. This particular in-store sample potentially excludes online shoppers, leading to what we call sampling bias, where not all sections of the population are equally represented.

Shopping behaviors can differ significantly depending on whether a customer chooses to shop in-store or online. **In-store shoppers** might spend longer browsing, touching products, and interacting with staff, possibly resulting in higher purchase volumes.
On the other hand, **online shoppers** might have a different approach. They could opt for more targeted shopping, focusing on getting specific items quickly rather than exploring new ones.
These behavioral differences can lead to variations in purchase amounts. Understanding these differences is essential when designing a study or collecting data to ensure both groups are adequately represented to avoid skewed results.

Average purchase volume is a metric used to reflect the typical amount spent by customers during their shopping trips. In our example, calculating the average involves summing all purchase volumes collected from a sample and dividing by the number of customers sampled.
However, if the sampled group doesn't reflect the entire population, such as only surveying in-store rather than both in-store and online shoppers, this average might not be representative of actual spending trends across all customers.
For a true measure, the sample needs to include purchase data from both in-store and online shoppers to account for different purchasing behaviors. This captures a comprehensive view, mitigating the risk of bias and leading to more accurate business insights into customer spending habits.