What is the difference between descriptive and inferential statistics?

Descriptive statistics and inferential statistics serve distinct but complementary purposes in the field of statistics. Descriptive statistics are utilized to summarize and describe the main features of a data set, providing straightforward summaries about the sample or population, including measures such as mean, median, mode, and standard deviation, as well as graphical representations like charts and graphs. This type of statistics gives us a clear view of what the data looks like and its key characteristics.

On the other hand, inferential statistics go a step further by using a random sample of data drawn from a population to make predictions or generalizations about that population. This often involves hypothesis testing, confidence intervals, and regression analysis, which allow us to determine relationships, make predictions, and infer characteristics of a larger group beyond the sampled data. The key to inferential statistics lies in its ability to draw conclusions and make estimations about a population based on the sample data.

The choice that states descriptive statistics summarize data from a sample, while inferential statistics make predictions about a population aptly captures the essence of their differences. It highlights the fundamental role of descriptive statistics in summarizing data versus the role of inferential statistics in making broader conclusions and predictions based on that data.