Which of the following methods can be used to identify outliers in a dataset?

The identification of outliers in a dataset is effectively accomplished using statistical methods, including Z-scores and the Interquartile Range (IQR). These techniques provide quantifiable thresholds to differentiate outliers from the rest of the data.

Using Z-scores, you can determine how many standard deviations a data point is from the mean. Typically, a Z-score above 3 or below -3 suggests that the data point is an outlier. On the other hand, the IQR method involves calculating the first and third quartiles of the dataset, from which the IQR is derived. Any data points that lie beyond 1.5 times the IQR from the quartiles are considered outliers. Both of these statistical methods rely on robust mathematical principles, making them reliable for outlier detection.

In contrast, relying solely on descriptive statistics does not provide the necessary tools for identifying outliers, as these statistics focus primarily on summarizing the main characteristics of the data without directly addressing the extremities that define outliers. Visual aids, while helpful in providing an overview and illustrating data distributions, may lack the precision needed for definitive outlier identification on their own. Lastly, dataset size reduction techniques do not focus on identifying outliers; rather, they aim to