In data cleaning, which technique is most effective in handling outliers in a dataset that could skew analysis?

A logarithmic transformation is often applied to datasets with outliers, as it compresses the data range, bringing outliers closer to the central data values. This approach is particularly useful for highly skewed data, as it minimizes the impact of extreme values on the overall analysis. Unlike simply removing or replacing outliers, which might distort the data or lose valuable information, a logarithmic transformation allows for retaining all values while reducing the skewness and making the data more normal-like for statistical analysis. Log transformation is a powerful tool for handling outliers without compromising the integrity of the dataset. The other options are incorrect because: • Option 1 is inaccurate; removing outliers may lead to loss of information, especially if these values are genuine and insightful. • Option 2 can reduce variability but may distort data accuracy, particularly if the mean is not representative of most data. • Option 4 suggests ignoring outliers, which can misrepresent results as extreme values may influence key insights if left unaddressed. • Option 5 confuses duplicates with outliers, as duplicates do not represent extreme values and require a separate approach.