In a data science project, what stage involves testing hypotheses?

The stage that involves testing hypotheses is data analysis. This phase is critical as it encompasses the application of statistical methods and algorithms to examine the data collected. During data analysis, hypotheses formed during the exploratory stages are rigorously tested to determine their validity based on the data at hand.

In this stage, various techniques such as statistical tests, regression analysis, and machine learning algorithms are used to evaluate the relationships between variables or to assess the significance of patterns found in the data. Testing hypotheses helps data scientists draw conclusions and make inferences that can inform decision-making or further research.

Data collection, on the other hand, focuses on gathering relevant information that will be analyzed later. Data cleaning is essential for preparing the data for analysis by removing inaccuracies or inconsistencies but does not involve testing hypotheses directly. Data visualization serves to communicate insights and findings derived from the analysis but does not include the hypothesis testing process itself. Thus, data analysis is the correct stage for hypothesis testing in a data science project.