Final answer:
One step in a data cleaning workflow is analyzing and interpreting data, involving the use of statistical methods to understand raw data and determine its support for a hypothesis.
Step-by-step explanation:
One of the steps in a typical data cleaning workflow is analyzing and interpreting data. This step involves reviewing the raw data collected, such as measurements or observations, and using statistical methods to make sense of it. It is a crucial step because it allows you to determine whether the raw data effectively supports a hypothesis or outcome. For instance, if scientists collect data on bacterial growth, they must analyze and interpret the measurements to ascertain if the results provide evidence that supports their hypothesis. This could involve identifying outliers, trends, and patterns within the data.
Additionally, data cleaning may include tasks such as removing duplicates, correcting errors, and dealing with missing values. These tasks ensure that the data is of high quality and ready for further analysis or modeling.