You're conducting a study and have encountered missing data. You opt for the model-based method for imputation. Under what circumstances might this approach introduce bias?
- If the chosen model fits poorly to the data
- If the chosen model is a complex one
- If the chosen model is a perfect fit for the data
- If the missing data is missing completely at random
Bias might be introduced in model-based imputation if the chosen model fits poorly to the data. If the model used does not reflect the true data generation process, the imputed values might be systematically biased, leading to incorrect conclusions.
Loading...
Related Quiz
- Which type of data analysis helps the most in feature selection for Machine Learning?
- What range of values does a dataset typically have after Min-Max scaling?
- What role does EDA play in formulating hypothesis or model selection in data analysis?
- A ___________ skewness indicates that the data distribution is skewed to the left.
- You are given a dataset with a high number of features. The computational resources are limited. What feature selection method might you consider?