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No one “perfect” method exists for filling in missing data; You can view this one picture as a starting point with some suggestions, rather than an absolute. You may want to decide beforehand if you care about statistical power or uncertainty; If you do, you'll want to lean towards one of the more complex routes (like multiple imputation), rather than a single imputation method--even if your data is linear or follows another trend or distribution shape.

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Large Enough Sample

Shapes of Distributions


Appropriately Handling Missing Values for Statistical Modelling and...

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