What is hypothesis testing used for in statistics?

Hypothesis testing is primarily employed to evaluate a hypothesis concerning a population parameter. The process involves formulating a null hypothesis and an alternative hypothesis to assess whether there is enough statistical evidence in a sample of data to infer that a certain condition holds true for the entire population. This method allows researchers to make inferences about population characteristics while accounting for variability and uncertainty.

The core of hypothesis testing lies in comparing the observed data against what would be expected if the null hypothesis were true. This comparison yields a p-value, which helps determine whether to reject or fail to reject the null hypothesis based on a pre-determined significance level. This technique is integral to making informed decisions and conclusions in statistical analysis.

Different options touch on related but distinct concepts. While determining relationships between variables is important in statistics, it is more closely associated with techniques like correlation and regression analysis rather than hypothesis testing directly. Similarly, gathering data from a sample pertains to sampling methods and data collection techniques, which are preliminary steps leading into analysis rather than the hypothesis testing process itself. Predicting future outcomes based on historical data relates more to predictive analytics and modeling, which also diverges from the primary function of hypothesis testing in assessing statistical significance of a claim about a population parameter.