Final answer:
In a data graph, 'aberration' refers to an error or deviation from the norm, which is an outlier. Outliers are data points that stand out as different from the rest of the dataset and can be errors or indications of something significant.
Step-by-step explanation:
The term aberration in the context of data in a graph typically refers to the concept of an error or deviation from the norm (option 1), which is clearly different from the established pattern or trend of the dataset. In statistics and data analysis, anomalies such as aberrations are often termed outliers. Outliers are notable because they do not fit the pattern seen in the bulk of the data and can potentially be due to errors, unusual but accurate measurements, or indications of a new trend starting to appear. These outliers can sometimes provide valuable insights into the data, despite their deviation from other data points.
Examples of Outliers
- An outlier can be simply a data entry error, like an extra zero added to a number by mistake, making it much larger than it should be.
- It can also be a legitimate data point that indicates a rare or extreme occurrence that's unusual compared to the rest of the data.
- In statistical graphs such as scatterplots, outliers might be those points that lie more than two standard deviations away from the best-fit line.
Outliers warrant further investigation to determine whether they are due to measurement errors or they represent valuable exceptions in the data that should be studied separately.