How are techniques that allow conclusions about variable relationships in a sample defined?

The correct answer is inferential statistics, which refers to the techniques used to draw conclusions about populations based on sample data. This branch of statistics allows researchers to make generalized statements about the relationships between variables in a larger group, beyond just the sample they have observed.

Inferential statistics employs methods such as hypothesis testing, confidence intervals, and regression analysis, enabling the assessment of relationships and the estimation of parameters with a measure of certainty about how these findings can be applied to a broader context. This is crucial in healthcare research, where it is often impractical or impossible to collect data from every individual in a population.

Descriptive statistics, on the other hand, focuses on summarizing and describing the features of a dataset without making predictions or inferences about a larger group. While it provides valuable insights into the sample itself, it does not extend conclusions to the population.

Predictive statistics typically refers to methods used to forecast outcomes based on data, while explanatory statistics might be thought of in terms of providing insights into causal relationships or understanding phenomena. However, these concepts do not specifically capture the essence of making inferences about relationships based on samples, as inferential statistics does.