What defines an outlier in a dataset?

An outlier in a dataset is defined as a data point that is significantly different from the other observations. This can mean that it is much higher or lower than the majority of the data points, indicating that it falls outside the expected range or distribution of the data. Outliers can arise due to variability in the data, measurement errors, or they may indicate something relevant about the data that could be worth exploring further. Identifying outliers is essential in data analysis because they can influence statistical calculations, such as mean and standard deviation, and may also affect predictive modeling.

In contrast, choices that imply conforming with the dataset or triviality do not accurately capture the essence of what an outlier represents. A repeated data point doesn’t necessarily indicate significance and does not define any deviation from norms. Thus, the definition capturing the notion of being significantly different from the rest is the most accurate and relevant to understanding outliers.