You have a dataset with missing values and you've chosen to use multiple imputation. However, the results after applying multiple imputation are not as expected. What factors might be causing this?
- Both too few and too many imputations
- The model used for imputation is perfect
- Too few imputations
- Too many imputations
If too few imputations are used in multiple imputation, the results may not be accurate. This may lead to an underestimation of standard errors and incorrect statistical inference. Increasing the number of imputations generally leads to more accurate results.
Loading...
Related Quiz
- How does improper handling of missing data impact the precision-recall trade-off in a model?
- The ______ of a scatter plot may indicate the presence of outliers in the dataset.
- Which measure of central tendency is calculated by adding all the numbers and dividing by the number of numbers?
- The primary goal of EDA is to ________.
- How can extreme outliers impact the interpretation of the skewness of a dataset?