In what scenarios could mean/median/mode imputation lead to a misleading interpretation of the data?
- When data has many outliers
- When data is missing completely at random
- When data is missing systematically
- When data is normally distributed
Mean/median/mode imputation could lead to a misleading interpretation of the data when data is missing systematically or 'not at random'. This is because this kind of imputation might introduce bias by not accurately reflecting the reasons behind the missingness and could distort the true distribution of the data.
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