Handling Missing Data in Data Cleaning: Best Practices and Methods

Out of the options provided, 'i) Fill in a best estimate manually' and 'ii) Use the average value' are possible methods in dealing with missing data during data cleaning.

'i) Fill in a best estimate manually': This method involves manually estimating and filling in the missing values based on domain knowledge or expert judgment. It requires careful consideration and understanding of the data to provide the most appropriate estimates for the missing values.

'ii) Use the average value': This method involves replacing the missing values with the average value (mean, median, or mode) of the available data for that particular variable. It provides a simple way to fill in missing values based on the overall central tendency of the variable.

'iii) Don't fill in, just leave it blank': This option suggests not filling in the missing values and leaving them as blank or NA values. While this is a valid approach in some cases, it may limit the usability of the dataset for certain analyses that require complete data.

It's important to note that the choice of method depends on the specific dataset, the nature and extent of missingness, and the goals of the analysis. Different methods may be more appropriate in different situations.