Outliers can have a major influence on a dataset by distorting statistical measurements like the mean and standard deviation, resulting in erroneous representations of central tendency and variability. They can also skew correlation and regression studies, reducing the trustworthiness of prediction models. Furthermore, outliers might impact decision-making processes, resulting in inaccurate findings or misleading interpretations. Outliers must be handled effectively in order for analyses to remain valid and provide reliable insights. Techniques like eliminating, converting, or compensating for outliers can assist to reduce their influence and increase the robustness of data analysis.
