Which type of statistics allows researchers to make inferences about a population based on sample data?

Inferential statistics is the branch of statistics that enables researchers to draw conclusions and make predictions about a larger population based on data collected from a sample. This is crucial because it is often impractical or impossible to collect data from an entire population due to constraints such as time, cost, or accessibility. Through techniques such as hypothesis testing, confidence intervals, and regression analysis, inferential statistics provide a framework for understanding patterns in the sample data and generalizing these findings.

In contrast, descriptive statistics focuses only on summarizing and describing the characteristics of a data set. It provides insights into the data through measures like mean, median, mode, and standard deviation but does not extend beyond the specific data collected. Comparative statistics involves analyzing data from different groups or populations, which can be part of inferential statistics but does not encompass the broader inferential framework. Multivariate statistics examines multiple variables simultaneously to understand relationships and effects within a dataset but, like comparative statistics, remains a subset of inferential approaches without necessarily making population-wide inferences.

Thus, inferential statistics is the correct choice because it specifically pertains to making generalizations from a sample to a population.