Which term describes data points that lie significantly outside the expected range?

The term that describes data points lying significantly outside the expected range is "outliers." These are observations that differ markedly from the overall pattern of data in a dataset. In statistical analysis, identifying outliers is crucial because they can significantly skew results, lead to incorrect conclusions, and affect the efficacy of models or forecasts.

Outliers can result from variability in the data or may indicate experimental errors, sampling errors, or genuine anomalies that warrant further investigation. Recognizing outliers helps analysts understand the underlying structure of the data and can lead to insights about the processes generating the data.

Trends refer to the general direction in which data points move over time and do not indicate anomalies. Forecasts are predictions about future values based on historical data and trends rather than identifying unusual data points. Residuals are the differences between observed and predicted values in a regression analysis, rather than individual points deviating from expected ranges.