Difference between revisions of "Calculate Outliers"

In statistics, an ''outlier'' is a data point that significantly differs from the other data points in a sample. Often, outliers in a data set can alert statisticians to experimental abnormalities or errors in the measurements taken, which may cause them to omit the outliers from the data set. If they ''do'' omit outliers from their data set, significant changes in the conclusions drawn from the study may result. Because of this, knowing how to calculate and assess outliers is important for ensuring proper understanding of statistical data.

In statistics, an ''outlier'' is a data point that significantly differs from the other data points in a sample. Often, outliers in a data set can alert statisticians to experimental abnormalities or errors in the measurements taken, which may cause them to omit the outliers from the data set. If they ''do'' omit outliers from their data set, significant changes in the conclusions drawn from the study may result. Because of this, knowing how to calculate and assess outliers is important for ensuring proper understanding of statistical data.

== Steps ==

== Steps ==

#Learn how to recognize potential outliers. Before deciding whether or not to omit outlying values from a given data set, first, obviously, we must identify the data set's potential outliers. Generally speaking, outliers are data points that differ greatly from the trend expressed by the other values in the data set - in other words, they '''lie outside''' the other values. It's usually easy to detect this on data tables or (especially) on graphs. If the data set is expressed visually on the graph, outlying points will be "far away" from the other values. If, for instance, the majority of the points in a data set form a straight line, outlying values will not be able to be reasonably construed to conform to the line.

#Learn how to recognize potential outliers. Before deciding whether or not to omit outlying values from a given data set, first, obviously, we must identify the data set's potential outliers. Generally speaking, outliers are data points that differ greatly from the trend expressed by the other values in the data set - in other words, they '''lie outside''' the other values. It's usually easy to detect this on data tables or (especially) on graphs. If the data set is expressed visually on the graph, outlying points will be "far away" from the other values. If, for instance, the majority of the points in a data set form a straight line, outlying values will not be able to be reasonably construed to conform to the line.