This is the task that the SAS programmers had to perform when confronted with the huge data sets in their jobs. The designers and analysts at SAS were not just faced with the challenge of dealing with the massive amount of information that they need to analyze but also the problem of figuring out what these statistical anomalies mean. Whenever they fail to understand the information, it leads to much frustration and the need to run out of ideas. It would be much better if they could design an analysis that would deal with such anomalies as opposed to always leaving their work incomplete.

A critical part of the analysis that is designed for statistical anomalies is that it should be able to discover correlations that cannot be explained by randomness. To get a better understanding of how to develop such an analysis, we first need to have a good understanding of what statistical outliers really are.

Statistical outlier is a statistical classification of an observed data set that is outside the normal range of values that would be expected to be obtained by chance alone. These anomalies are usually generated by the poor design of the statistical model that has been used to classify the data set.

The designs of statistical models for different classifications of data sets often makes it impossible for a model to correctly classify the data. The application of data mining techniques on the data sets does not help as well as these methods may only classify the data according to their location in the list of “classifications” for the actual data.

By using algorithms to classify the data sets according to the expected distributions, it is possible to detect these statistical anomalies. There are many types of statistical anomaly that can be found in a database.

Some of the more common ones are outliers and statistics oddities. Statistics oddities are either the result of the way in which the data was collected or the way in which the data was analyzed. Outliers are typically the result of the failure of the statistical model that had been used to classify the data.

Often the data collectors will group data based on the design that they had decided to use to classify the data. They will then define a means to compare each group of data. Most often the comparison will be the means that will help them eliminate any statistical anomalies in their data sets.

Statistical anomalies arise as a result of the design or the analysis method of the statistical model. They may not have been intentionally introduced but in many cases they do appear due to the design flaws that had been used to classify the data sets.

Another method that is often used in data mining is the clustering method. In this method, the data set is first gathered into multiple sets. It is then grouped together based on the means of the classification.

However, if the same set of data is grouped again, it is likely that the statistical anomalies will appear again. This is because the clustering method is most effective when the data are first grouped according to the definition of the means.

Therefore, if the use of clustering is not used to classify the data, the statistical anomalies will most likely appear again in the cluster data set. Statistics outlier detection is the task that the SAS statistical programmers had to perform when faced with the huge data sets in their jobs.