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In statistics, the term "population" refers to the entire group that you want to draw conclusions about. In our case, the population is crucial to understanding rental patterns among students living off-campus. The population consists of all 12,304 students who live off-campus and have not yet graduated from the university.
The concept of population is essential because it defines the group you are interested in. It is important to remember that the population includes everyone in the specified group, not just those who provide data. Understanding this helps ensure that any findings or results apply to the entire group you are interested in.
A clear definition of the population is the first step in any statistical study. This clarity ensures that the data collected can help answer the research question or solve the problem being investigated.

A "sample" in statistics is a subset of the population from which you actually collect data. It is nearly always impractical to collect data from the entire population, so sampling provides a manageable way to gather insights.
In the exercise, the sample consists of the 78 students who returned their questionnaires. Although 200 students were initially selected, the actual sample only includes those who provided data back to the university's housing office.
A sample should ideally be representative of the population, capturing its diversity and characteristics. This representativeness ensures that conclusions or estimates drawn from the sample can credibly apply to the broader population.
It is important to understand that the sample can sometimes restrict the population about which you're obtaining information. As such, sampling methods are crucial to how accurate and reliable your data and predictions will be.

The process of data collection is about gathering and measuring information on variables of interest in a systematic way. It's important to choose the right data collection methods to ensure the validity and reliability of the data.
Mailed questionnaires, like in the exercise, are a common method; however, they often come with challenges, especially low response rates, as seen with only 78 out of 200 returned. This can lead to non-response bias, where those who don't respond might systematically differ in meaningful ways from those who do.
To mitigate such effects, researchers could consider combining methods, like face-to-face interviews or online surveys, to increase response rates and gain a fuller picture. Additionally, incentives can be used to encourage more participation.
Choosing the right data collection method is as important as determining the population and the sample. The method not only affects data quality but also how well the results can be generalized to the population.