Which statement best describes inferential statistics?

Which statement best describes inferential statistics?

• A. Measuring relationships between variables in quantitative terms
• B. Describing what something is like in qualitative terms
• C. Avoiding making causal inferences among their variables
• D. Measuring cause-and-effect relationships among two or more variables or factors in qualitative terms

Answer: (D)

Inferential statistics uses data from a sample to draw conclusions about a larger population, relying on probability to express uncertainty and to test ideas about relationships, differences, or effects. The statement that best describes inferential statistics among the options is the one about measuring relationships between variables in quantitative terms, because inferential methods quantify how variables relate to each other and allow these relationships to be generalized beyond the observed data. The other options describe qualitative description, or misstate the role of causal inference and the nature of the data, which don’t capture the essence of inferring population characteristics from quantitative sample data. In practice, you’d see this work when estimating a population mean, testing a treatment effect, or constructing a confidence interval to express uncertainty about an inferred parameter.

Inferential statistics uses data from a sample to draw conclusions about a larger population, relying on probability to express uncertainty and to test ideas about relationships, differences, or effects. The statement that best describes inferential statistics among the options is the one about measuring relationships between variables in quantitative terms, because inferential methods quantify how variables relate to each other and allow these relationships to be generalized beyond the observed data. The other options describe qualitative description, or misstate the role of causal inference and the nature of the data, which don’t capture the essence of inferring population characteristics from quantitative sample data. In practice, you’d see this work when estimating a population mean, testing a treatment effect, or constructing a confidence interval to express uncertainty about an inferred parameter.