What is hypothesis testing?

Hypothesis testing is a fundamental method in statistics that involves making inferences or drawing conclusions about a population based on sample data. It is primarily used to determine whether there is enough evidence to support a specific claim or hypothesis regarding a population parameter.

The correct option describes hypothesis testing as a method of making statistical decisions using experimental data. This process typically involves establishing a null hypothesis (a statement that there is no effect or no difference) and an alternative hypothesis (which posits that there is an effect or a difference). Researchers collect data through experiments or observations and then apply statistical tests to evaluate the evidence against the null hypothesis. If the statistical analysis shows that the observed data is highly unlikely under the assumption of the null hypothesis, researchers may reject the null hypothesis in favor of the alternative hypothesis.

In contrast, some of the other options focus on different aspects of data and statistics. For example, data visualization is about representing data graphically to identify patterns and trends, which is not the same as making statistical decisions. Similarly, data storage pertains to how data is managed and saved, and establishing data credibility involves assessing the trustworthiness of the data itself rather than testing hypotheses. Therefore, the core function of hypothesis testing lies in its role in decision-making based on statistical evidence