What is the purpose of inferential statistics?

The purpose of inferential statistics is to make predictions about a population based on sample data. This branch of statistics allows researchers to draw conclusions and make generalizations beyond the immediate data set they have. By using techniques such as hypothesis testing, confidence intervals, and regression analysis, inferential statistics enable analysts to assess the likelihood that a sample reflects the characteristics of a larger population.

This is particularly important in fields such as criminal justice research, where it is often impractical or impossible to collect data from the entire population due to resource constraints. By analyzing a representative sample, researchers can infer trends, relationships, and patterns that may exist within the broader population, thereby providing valuable insights that inform policy and practice within the criminal justice system.

The other options do not capture the essence of inferential statistics: describing sample data refers to descriptive statistics; summarizing data with visuals pertains to data visualization techniques; and exploring personal narratives falls outside the domain of statistical analysis.