What statistical analysis tool would be used for testing hypotheses?

Inferential statistics is the appropriate statistical analysis tool used for testing hypotheses. This branch of statistics allows researchers to make generalizations about a population based on samples drawn from it. It involves methods that utilize sample data to infer properties about a larger population, which is a cornerstone for hypothesis testing.

When testing hypotheses, inferential statistics provides the framework to assess the likelihood that a hypothesis about a population parameter holds true. Techniques such as t-tests, chi-square tests, ANOVA, and confidence intervals are all part of inferential statistics and are specifically designed for hypothesis testing. This method enables analysts to determine whether there is enough evidence to accept or reject a null hypothesis, thus supporting decision-making in business and research contexts.

Descriptive statistics, on the other hand, summarize or describe the characteristics of a dataset but do not allow for conclusions beyond the data at hand. Regression analysis focuses on modeling relationships between variables rather than testing a hypothesis directly. Time-series analysis examines data points collected or recorded at specific time intervals, which may not necessarily relate to hypothesis testing but could be part of inferential analyses.