Data selection is the process of selecting data from one or more sources and placing it into a common format for further analysis. It is a critical step in the data analysis process, since the output from this process can be used to generate insights and drive decisions. There are several different approaches that can be taken when it comes to data selection, so it is important to understand the key principles behind each one.

One of the most common statements regarding data selection is that data should be collected from a representative portion of the population. This means that the data should be a random sample that is representative of the population in terms of age, gender, race, geographical area, income level, and other appropriate characteristics. This allows for accurate results to be generated from the analysis that can be generalized to the entire population.

Another important statement regarding data selection is that data should be collected from reliable sources. This means that the data should come from a source that is known for providing accurate and up-to-date information. This is particularly true for sources of quantitative data, such as surveys and market research. Additionally, data should be collected in a way that allows for the data to be verified and validated.

Yet another statement concerning data selection is that data should be collected in a way that minimizes bias. This means that the data should be collected in a way that does not impact the results of the analysis. For instance, if the data is collected from a self-selected survey, it is important to make sure that the items offered on the survey do not lead the respondent to a certain answer. Additionally, it is important to make sure that the sampling process is unbiased to ensure that the results are reflective of the entire population.

Ultimately, which of the following statements is true concerning data selection will depend on the particular situation and the purpose of the analysis. However, regardless of the situation, understanding the key principles of data selection is essential for accurate results to be generated from the analysis.