What is hypothesis testing primarily used for?

Hypothesis testing is primarily used to evaluate a claim or hypothesis about a population parameter based on sample data. This statistical method allows researchers to make inferences about population characteristics by analyzing a representative sample, thus determining whether there is enough evidence to support or refute the initial claim, known as the null hypothesis.

Through this process, researchers can obtain a p-value which indicates the probability of observing the sample data if the null hypothesis were true. If the evidence is strong enough—usually assessed by comparing the p-value to a predetermined significance level—researchers can reject the null hypothesis in favor of an alternative hypothesis. This process is crucial for making informed conclusions in various fields, including psychology, as it provides a systematic way to decide whether observed effects are statistically significant or likely due to random chance.

In contrast, confirming that a hypothesis is valid does not accurately describe the objective of hypothesis testing, as validity cannot be guaranteed. Calculating the mean of a dataset is a basic statistical operation and does not encompass the broader goal of hypothesis testing, while establishing the null hypothesis is part of the methodology but not the primary purpose of hypothesis testing itself.