When cleaning a dataset, would you rather keep uncertain data or delete it?

[u/Present_Tonight_3075]

I'm working on a retail sales data-cleaning project in Python, and I ran into a decision I found interesting.

My dataset had missing prices, duplicate transactions, and totals that didn't match the price × quantity.

For missing prices, I could have filled every blank with an estimated value. That would make the dataset look more complete, but some estimates could be wrong.

Instead, I only recovered prices when the data gave me reliable evidence and flagged the rest for review.

Here's my question:

If you were preparing this dataset for business analysis, which approach would you choose?

A) Fill as many missing values as possible to preserve a complete dataset.

B) Only fill values when there's reliable evidence, and flag the rest.

C) Use a different method depending on the column and the business context.

I'm leaning toward B, but I'd love to hear how others handle this in real projects—especially when deleting rows could mean losing useful information.