What is the primary function of statistical hypothesis testing in analytics?

The primary function of statistical hypothesis testing is to assess statements about population parameters using sample data. This involves formulating null and alternative hypotheses to make inferences about a population based on the analysis of sample data. Essentially, hypothesis testing allows analysts to evaluate whether the observed evidence from a sample is strong enough to support a claim about the larger population from which the sample was drawn.

In practice, this method helps determine whether the effects or differences observed in sample data can be generalized to the population, taking into account the variability inherent in sampling. By utilizing concepts such as significance levels and p-values, analysts can statistically decide whether to reject the null hypothesis in favor of the alternative hypothesis based on the evidence provided by the sample.

The other options are not aligned with the primary function of hypothesis testing. Summarizing data for reporting is more about descriptive statistics rather than making inferences about a population. Establishing budgets for marketing strategies pertains to financial planning, rather than statistical analysis. Determining sample size is related to the design of experiments and studies, which precedes conducting hypothesis testing but does not directly involve making inferences based on sample data. Thus, the best choice clearly highlights the role of hypothesis testing in analytics.