What is inferential statistics used for?

Inferential statistics is primarily used to draw conclusions about a larger population based on the analysis of a sample from that population. This involves using data collected from a sample to make inferences or predictions about the total population, allowing researchers to understand trends, test hypotheses, and estimate population parameters.

This process typically includes techniques such as confidence intervals, hypothesis testing, and regression analysis, which all rely on sample data to infer characteristics and behaviors of the larger group. By applying inferential statistics, researchers can make reasoned generalizations without needing to gather data from every individual in the population, which is often impractical or impossible.

The other choices highlight different aspects of data analysis. Summarizing data characteristics pertains to descriptive statistics, which focus on organizing and summarizing data without making inferences. Exploring data visually refers to graphical representations, which help in understanding the data's distribution and patterns. Conducting qualitative interviews relates to qualitative research methods, which aim to understand concepts, thoughts, or experiences rather than quantify characteristics through statistical means. Thus, the key distinction of inferential statistics lies in its role in extrapolating findings from a sample to a broader context.