What does the term "inferential statistics" refer to?

Inferential statistics refers to techniques used to make predictions or inferences about a larger population based on the analysis of a sample taken from that population. This approach allows researchers to draw conclusions beyond merely summarizing the data collected, extending findings to a broader context. For example, if researchers conduct a survey on a sample of voters, they can use inferential statistics to make predictions about the voting intentions of the entire population based on the responses from that sample.

The process typically involves using probability theory to account for the uncertainty inherent in sampling. This enables researchers to estimate population parameters, test hypotheses, and make decisions that can guide business strategies and policies.

In contrast to this, summarizing data from a specific sample would be an example of descriptive statistics, which provides insights into the characteristics of the sample itself rather than extending those insights to the population. Graphical representations of data also fall under descriptive statistics, aiding in visual understanding but not making predictions about larger groups. Analyzing qualitative data focuses on non-numeric insights and interpretations, which is distinct from the quantitative methods utilized in inferential statistics.