Final answer:
Filling in missing values with zeros is quick and straightforward but can skew data distribution and introduce bias. It may not accurately reflect the underlying data generating process and can affect statistical measures and machine learning model performance.
Step-by-step explanation:
Imputation for missing values by filling in with zeros has several advantages and disadvantages. One of the advantages is that it can be a quick and easy method to handle missing data, allowing algorithms to process datasets without errors due to missing values. This approach can work particularly well when the zeros can be interpreted meaningfully within the context of the data, such as indicating the absence of an event or property.
However, there are significant disadvantages to this method of imputation. A major concern is that it can skew the distribution of the data, leading to incorrect statistical analyses. For example, if the original distribution of the data was not centered around zero, imputing zeros can shift the mean and affect measures like variance and standard deviation. Additionally, this method can introduce a bias since zeros can be interpreted as actual observations, which may not be a true reflection of the underlying data generating process. This technique can also adversely impact the performance of some machine learning models by creating an artificial increase in the frequency of zero values.