meanimputointi

Mean imputation is a statistical technique used to handle missing data in a dataset. It involves replacing each missing value in a particular variable with the mean of the observed values for that same variable. This method is simple to implement and computationally inexpensive.

While straightforward, mean imputation has several drawbacks. It can distort the distribution of the variable, particularly if a large proportion of the data is missing. The variance of the imputed variable will be underestimated, and the correlation between variables may be weakened. Furthermore, it can lead to biased parameter estimates in statistical models.

Despite these limitations, mean imputation can be a reasonable choice when the amount of missing data is very small and the variable is not crucial for the analysis. It is often used as a baseline method to compare against more sophisticated imputation techniques. It's important to consider the potential impact of mean imputation on the results of any subsequent analysis. Other methods like median imputation or regression imputation are often preferred for more robust handling of missing data.