Which of the following is a common method for handling missing data?

Imputation is widely recognized as a fundamental technique for addressing missing data within datasets. This method involves estimating and substituting the missing values based on the available data, which can significantly enhance the quality and integrity of the dataset for analysis.

Various approaches to imputation exist, ranging from simple techniques, such as filling in missing values with the mean, median, or mode of the existing data, to more complex methods, such as using regression or machine learning algorithms to predict the missing values based on correlations found within the dataset. Proper imputation is critical because neglecting missing data can lead to biased analyses and inaccurate conclusions.

In the context of data science, effectively managing missing data via imputation allows analysts to maintain the robustness of their models and improve the reliability of predictions derived from the data. This is essential for ensuring that machine learning algorithms function optimally, as many algorithms cannot handle missing values natively.

Data normalization, outlier detection, and differencing all serve different purposes in data preparation and analysis. Normalization adjusts the scale of data for comparison, outlier detection identifies and handles extreme values that can skew results, and differencing is a method used primarily in time series analysis to remove trends and stabilize data variance. While each of these methods has its place