Given the following data set: Which of the following steps would be MOST appropriate to cleanse the data set?

Removing duplicate data is a critical initial step in data cleansing as it ensures that each record is unique, preventing overcounting or misrepresentation in analyses. Duplicate entries can arise from various sources and must be addressed to maintain data quality and accuracy.

Removing outliers is a step in data preprocessing often done after initial cleansing, focusing on extreme values rather than fundamental record uniqueness.

Verifying the data is a broader process of checking accuracy against sources, not a specific cleansing action for an initial pass.

Adding missing values, also known as imputation, is a technique to handle incomplete data, but it typically follows the identification and removal of duplicate records.