What is the difference between descriptive and inferential statistics?

Descriptive statistics and inferential statistics serve distinct but complementary roles in the field of data analysis. The distinction lies primarily in their functions and applications.

Descriptive statistics involve methods for summarizing and organizing data. This includes measures such as mean, median, mode, and standard deviation, which provide a clear view of the data set's central tendencies and variability. Their primary aim is to give a comprehensive overview of the data collected, allowing researchers to describe characteristics without extending beyond the data itself.

On the other hand, inferential statistics focus on making predictions or generalizations about a larger population based on a sample of data. Using tools such as hypothesis testing, confidence intervals, and regression analysis, inferential statistics allow researchers to draw conclusions and infer patterns or relationships that are likely to exist in the population. This is particularly valuable when it is impractical to gather data from the entire population.

Therefore, the first statement accurately captures this distinction: descriptive statistics only summarize the data collected, while inferential statistics use that data to make predictions or inferences about a larger population.