Handling missing data is about balancing bias, information retention, and reproducibility. Start by figuring out why data are missing, because the reason guides which methods are appropriate. If data are missing due to random chance or related to observed factors (a missingness mechanism like MCAR or MAR), you can use imputation methods that leverage available information. Choosing a domain-appropriate imputation means picking techniques that respect the data type and relationships in your field—simple mean substitution is often inadequate, while approaches like regression-based imputation or multiple imputation can provide less biased estimates by reflecting uncertainty about the missing values. When imputation isn’t suitable, it’s important to remove or flag problematic records in a transparent way so analyses aren’t distorted, and to clearly document the assumptions you’re making about the missing data. Finally, perform sensitivity analyses to see how results change under different missing-data assumptions or imputation methods; if conclusions hold across scenarios, confidence increases. Simply ignoring missing data or discarding all rows with gaps wastes information and can bias results, and assuming missing values are zero can drastically distort distributions and relationships.