What technique is used in inferential statistics to draw conclusions about a population?

Sampling is a fundamental technique in inferential statistics that allows researchers to draw conclusions about a larger population based on a subset of that population. By selecting a sample, statisticians can gather data and analyze it to make inferences about the population's characteristics, trends, or behaviors. This approach is vital because it is often impractical or impossible to study an entire population due to constraints like time, resources, and accessibility.

Once a sample has been obtained, various statistical methods, including hypothesis tests or confidence intervals, can be applied to analyze the data. This analysis can help in understanding the probability of certain outcomes occurring within the broader population.

The other techniques listed serve different purposes. For instance, regression analysis is primarily concerned with modeling relationships among variables, while data visualization focuses on presenting data in a graphical format, and correlation testing assesses the strength and direction of relationships between two variables. These methods do not specifically aim at making population-level inferences directly from sample data, which is the core function of sampling in inferential statistics.