<ul><li>Outliers, known as non-robust estimators, can significantly impact data analysis by skewing averages and leading to incorrect conclusions.</li><li>Outliers inflate variance and confidence intervals, causing models to perceive data as more variable than it actually is, resulting in flawed forecasts and testing outcomes.</li><li>Outliers can violate model assumptions such as normality and equal variance, leading to biased parameter estimates and misleading feature importance.</li><li>Outliers hide true patterns in data, obscuring trends, seasonality, and periodicity, making it challenging to identify meaningful insights.</li></ul>

Why Outliers Are a Major Headache in Data Analysis

Discover more