What is a potential consequence of not addressing outliers in a dataset?

Not addressing outliers in a dataset can lead to inaccurate extrapolations because outliers can significantly skew the results of statistical analysis. When outliers are present, they can distort the mean and other statistical measures, leading to misleading conclusions about the data. For instance, if an outlier is much higher or lower than the rest of the data, it can affect regression lines, averages, and predictions based on these models, resulting in conclusions that do not truly reflect the trends or patterns of the majority of the data. This is crucial for making accurate predictions and informed decisions based on the data, as any predictions or projections made from an influenced model may fail when applied to the general population. Hence, recognizing and appropriately handling outliers is essential for maintaining the integrity and reliability of the analysis.