What is an abnormal?

In statistics, an abnormal, also called an outlier, is a data point that significantly deviates from the other data points in a dataset. Abnormals can signify a number of things, including measurement errors, data entry mistakes, or the presence of unusual observations that may not be representative of the overall population.

Abnormals can be identified graphically, by visually inspecting a plot of the data. They can also be identified numerically, by calculating a measure of statistical deviation, such as the z-score or the standardized residual.

The presence of abnormals in a dataset can have an impact on the results of statistical analysis, and it is important to consider their potential impact when conducting data analysis. In some cases, abnormals may need to be removed from the dataset prior to analysis, while in other cases they may be retained as valuable information points.

Here are some common examples of abnormals:

* In a dataset of student test scores, an abnormally high score may be due to cheating, while an abnormally low score may indicate a student who was not prepared for the test.

* In a dataset of sales figures, an abnormally high sale may be due to a special promotion or a one-time sale, while an abnormally low sale may indicate a store that is struggling.

* In a dataset of medical data, an abnormally high or low reading may indicate a medical condition that requires further investigation.

It is important to note that not all abnormals are the result of errors or unusual observations. In some cases, abnormals may be caused by legitimate changes in the underlying population. For example, in a dataset of stock prices, an abnormally high price may be due to a positive earnings report, while an abnormally low price may be due to bad news.

Therefore, it is important to carefully investigate abnormals before making any conclusions about their significance.