In a normal distribution, how much would two standard deviations represent in square footage?

In a normal distribution, one of the key concepts is the role of standard deviations in understanding the spread of data around the mean. The majority of data points in a normal distribution fall within certain ranges of the mean, specifically within one, two, and three standard deviations.

When defining the standard deviation, it is a measure of how much individual data points typically differ from the mean of a dataset. In the context of the question, if we assume that the data set follows a normal distribution regarding home sizes, saying that two standard deviations encompass a certain range of values is essentially quantifying the dispersion of square footage.

Choosing a specific option that refers to 240 square feet as the representation of two standard deviations suggests that this is the calculated measurement of variability we are interested in for the homes' square footage. The reason this answer makes the most sense in the context of a normal distribution is that it reflects the incremental increase of data spread around the mean, adhering to statistical principles associated with standard deviations.

In this scenario, understanding that two standard deviations typically capture approximately 95% of the data in a normal distribution helps clarify why this specific measurement is considered meaningful. Thus, when presented with options, referring to a specific square footage that represents this dispersion, like