medelvärdesbehandling

Medelvärdesbehandling, or mean imputation, is a statistical technique used to handle missing data in a dataset. When a value is missing for a particular variable, mean imputation replaces that missing value with the mean of the observed values for that same variable. This is a simple and widely used method due to its ease of implementation.

The process involves calculating the arithmetic mean of all the available data points for a specific feature or column. Once this mean is computed, every instance where a value is missing for that feature is filled with this calculated mean. This ensures that the dataset remains complete, which is often a prerequisite for many statistical analyses and machine learning algorithms.

However, mean imputation has limitations. It can distort the distribution of the variable by reducing its variance. This is because replacing missing values with the mean pulls the data points towards the center, making the spread of the data smaller than it truly is. Furthermore, it can lead to biased estimates of correlations between variables. Despite these drawbacks, mean imputation is sometimes preferred when the proportion of missing data is small, or as a quick preliminary step before employing more sophisticated imputation methods. It is also important to note that the mean is only appropriate for numerical data; for categorical data, the mode (the most frequent value) is typically used instead, a process sometimes referred to as modal imputation.