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In statistics, the Q test is used for identification and rejection of outliers. This test should be used sparingly and never more than once in a data set. To apply a Q test for bad data, arrange the data in order of increasing values and calculate Q as defined:
Q = Qgap/Qrange
Where Qgap is the absolute difference between the outlier in question and the closest number to it. If Qcalculated > Qtable then reject the questionable point.
|Number of values:|| 3|| 4|| 5|| 6|| 7|| 8|| 9|| 10|
| Q90%:|| 0.941|| 0.765|| 0.642|| 0.560|| 0.507|| 0.468|| 0.437|| 0.412|
| Q95%:|| 0.970|| 0.829|| 0.710|| 0.625|| 0.568|| 0.526|| 0.493|| 0.466|
For the data:
Arranged in increasing order:
Outlier is 0.169. Calculate Q:
With 10 observations at 90% confidence, Qcalculated < Qtable. Therefore keep 0.169 at 90% confidence.
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