The correct answer is: A. Data Imputation
Data imputation is the process of replacing missing values in a dataset with estimated values. This can be done using a variety of methods, such as the mean, median, or mode of the remaining values in the dataset. Data imputation is important because it can help to improve the accuracy of statistical analysis.
Data transformation is the process of changing the scale or form of a dataset. This can be done for a variety of reasons, such as to make the data more normally distributed or to reduce the influence of outliers. Data transformation can also be used to make the data more visually appealing.
Data visualization is the process of representing data in a graphical or pictorial form. This can be done for a variety of reasons, such as to make the data easier to understand or to identify patterns in the data. Data visualization can also be used to communicate the results of statistical analysis.
Data scaling is the process of adjusting the values in a dataset so that they have a common scale. This can be done for a variety of reasons, such as to make the data easier to compare or to reduce the influence of outliers. Data scaling can also be used to make the data more compatible with different statistical methods.
In the context of the question, data imputation is the most common technique for handling outliers. This is because data imputation can help to reduce the impact of outliers on the results of statistical analysis.