Deciding whether to clean or ignore problematic data points in a dataset depends largely on the specific situation and the potential impact of those data points on your analysis or model. First, you need to assess the nature of the problematic data. Common issues include missing values, outliers, and incorrect entries. If the problematic data can lead to significant biases or distortions in your results, it's usually best to clean it. For example, if you're working with a dataset of user ages and find a value like "999," this is clearly an error and should be addressed.
Next, consider the size of the problematic data relative to the total dataset. If the problematic points are a small percentage of the total data and their removal won't significantly affect your results, you might choose to ignore them. For instance, in a dataset containing thousands of records, if ten entries are found to be erroneous, and they don’t represent any important category or trend, it might be more efficient to proceed without them. However, if the data points are central to your analysis, such as a few missing values in a financial dataset where each dollar counts, cleaning those data points becomes necessary.
Lastly, think about the intended use of the dataset. If you're developing a model for a critical application, like healthcare or finance, erring on the side of caution and cleaning the data makes sense; even small errors can lead to large repercussions. Conversely, if you're using the data for exploratory analysis where less precision is acceptable, ignoring trivial issues might be appropriate. Always document your decisions to ensure that you can justify the approach you chose for future stakeholders who may look at your work.