What is the difference between descriptive and inferential statistics?

Descriptive statistics and inferential statistics serve distinct purposes in the field of statistics. Descriptive statistics summarize and present the main features of a dataset, allowing researchers to understand the characteristics, patterns, and basic trends of collected data. This can include measures such as means, medians, modes, and standard deviations, or visual representations like graphs and charts.

In contrast, inferential statistics go a step further by using sample data to make predictions or inferences about a larger population. This often involves hypothesis testing, estimation, and making generalizations, which are crucial for drawing conclusions beyond the immediate data at hand. Inferential statistics enable researchers to make statements about a population based on a sample, thereby addressing questions of probability and uncertainty.

This distinction highlights why the assertion about descriptive statistics summarizing data while inferential statistics make predictions is accurate. Each type of statistics plays a vital role in research methodology, with descriptive statistics providing clarity on the data itself and inferential statistics allowing for broader conclusions and insights.