What is an "outlier" in a dataset?

An outlier in a dataset is defined as a data point that significantly differs from other observations. This means it is either much larger or much smaller than the majority of the data points in the dataset, which can indicate variability in the data, measurement errors, or novel insights about the dataset. Identifying outliers is crucial as they can influence statistical analyses, such as mean and standard deviation, and can provide valuable information that may warrant further investigation.

The other choices do not accurately capture the essence of what defines an outlier. A data point that is within the common range (the first choice) is not an outlier, as it falls within the expected distribution of values. A measurement error or typo in data (the second choice) might lead to an outlier, but not every outlier is due to such errors; some reflect genuine variability. Lastly, a value that fits well with statistical models (the fourth choice) indicates that the data point aligns with the expected patterns in the data, which fundamentally contradicts the concept of being an outlier. Thus, understanding outliers is essential for proper data analysis and interpretation.