Figure 3.

Choosing a value for Q. The value of Q determines how aggressively the method will remove outliers. This figure shows three possible values of Q with small and large numbers of data points. Each graph includes an open symbols positioned just far enough from the curve to be barely defined as an outlier. If the open symbols were moved any closer to the curve, they would no longer be defined to be outliers. If Q is set to a low value, fewer good points will be defined as outliers, but it is harder to identify outliers. The left panel shows Q = 0.1%, which seems too low. If Q is set to a high value, it is easier to identify outliers but more good points will be identified as outliers. The right panel shows Q = 10%. We recommend setting Q to 1% as shown in the middle panels.

Motulsky and Brown BMC Bioinformatics 2006 7:123   doi:10.1186/1471-2105-7-123
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