What are outliers in a data set?

Outliers are defined as values in a data set that are disproportionately large or small compared to the rest of the data. They stand out due to their extreme nature, which can significantly affect statistical analyses and interpretations.

In practical terms, outliers may indicate variability in the measurements, a mistake in data entry, or some underlying phenomenon that warrants further investigation. Identifying outliers is crucial because they can influence the results of statistical tests, skew means, and affect the general conclusions drawn from the data.

The other definitions do not appropriately capture what an outlier is. Values within the interquartile range are considered to be within a normal spread of data points and do not signify outliers. Similarly, values that follow a normal distribution would not typically include outliers, as they would conform to the expected pattern. Distinguishing outliers from values representing median outcomes is also critical, as medians reflect central tendencies rather than extreme deviations. Thus, understanding and identifying outliers help provide a clearer picture of the data set's overall pattern and potential anomalies.