Principal component analysis (PCA), as it reduces dimensionality and can indirectly highlight outliers by showing which components contribute most to the variance.

Creating scatter plot visualizations for each pair of features to visually inspect for outliers, despite the method's inability to quantify their impact.

Standard deviation analysis, which provides a statistical measure of how spread out the data points are around the mean, useful for identifying outliers as those beyond 3 standard deviations.

K-means clustering, which groups data into clusters based on similarity but is not designed for outlier detection or impact assessment.

Both standard deviation analysis and scatter plot visualization, to first quantify the impact of outliers and then visually confirm their presence.