What does the term "outlier" refer to in statistics?

The term "outlier" in statistics specifically identifies a value that is significantly higher or lower than the rest of the data points in a dataset. Outliers can indicate variability in the data or may suggest that there was an error in data collection. They often fall outside the expected range of values when analyzing data, which is why they stand out within a dataset.

For instance, if you have a dataset measuring the heights of students in a classroom and most students fall within the range of 150 to 170 cm, but one student is 210 cm tall, that 210 cm measurement would be considered an outlier. Recognizing outliers is essential for accurately interpreting statistical analyses, as they can skew results and affect measures like the mean.

The other options refer to different statistical concepts: a value equal to the mean represents central tendency, a value that appears most frequently describes the mode, and the median is the middle value of a dataset when arranged in order. Each of these terms has its own significance but does not align with the definition of an outlier.