IBM Data Science Test 2025 – 400 Free Practice Questions to Pass the Exam

The approach that addresses drawing conclusions about a population using sample data is inferential statistics. This branch of statistics is specifically designed to make inferences or predictions about a larger population based on a smaller subset of data, known as a sample.

Using inferential statistics, analysts can test hypotheses and estimate population parameters while accounting for the uncertainty that comes from using a sample instead of the entire population. Common techniques in inferential statistics include hypothesis testing, confidence intervals, and regression analysis. The goal is to determine whether the findings observed in the sample can be generalized to the broader population from which the sample was drawn.

In contrast, descriptive statistics focuses solely on summarizing and describing characteristics of a dataset without attempting to make predictions or conclusions about a larger population. It provides insights into data through measures such as the mean, median, mode, and standard deviation but does not extend beyond the data at hand. Measures of central tendency and measures of spread help to capture important properties of the data, but they do not facilitate generalization to a broader context. Similarly, variance and standard deviation are specific statistical measures that describe data variability but do not provide a framework for making inferences about a population.