Question

what is outliers

Answer 1

Outliers are data points that differ significantly from other observations in a dataset. They can fall far outside the range of the majority of the data and may indicate variability in the measurement, experimental errors, or novel insights about the data.

Outliers can be identified using various statistical methods. Common ways to detect them include:

1. **Z-Score:** A z-score measures how many standard deviations a point is from the mean. A z-score above 3 or below -3 is often considered an outlier.

2. **Interquartile Range (IQR):** This method determines outliers by calculating the IQR, which is the range between the first and third quartiles (Q1 and Q3). Outliers are typically defined as points that are less than Q1 - 1.5 * IQR or greater than Q3 + 1.5 * IQR.

3. **Box Plots:** Visual representations like box plots can help identify outliers by showing the spread and identifying points that fall outside the "whiskers."

Outliers can have a significant impact on statistical analyses, potentially skewing results if not addressed. Depending on the context, analysts may choose to investigate the causes of outliers, remove them, or treat them as valid data points that require special consideration.