What does handling missing data by imputation typically involve?

Handling missing data by imputation typically involves filling in missing values with a statistical measure. Imputation is a technique used to replace missing values in a dataset, allowing for a more complete and accurate analysis. This can include methods such as using the mean, median, or mode of the available data to fill in gaps. The advantage of using imputation is that it allows analysts to retain more data points, which can enhance the robustness of statistical analyses and machine learning models.

By utilizing imputation, analysts can maintain the size of their dataset without introducing biases that can occur through deletion or other methods. It also enables researchers and data scientists to utilize all available information, making for better decision-making based on comprehensive datasets.

In contrast, deleting rows with missing values may lead to loss of important information and potential bias if the missing data is not randomly distributed. Ignoring missing data could result in skewed analyses and incorrect conclusions. Creating duplicate records is not a legitimate method for handling missing data and would lead to inaccuracies in the dataset. Hence, filling in missing values with a calculated statistical measure is the preferred method for addressing missing data through imputation.