Know when to discard an outlier data point in HPLC by using the Q Test

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Know when to discard an outlier data point in HPLC by using the Q Test - How To

For example, if you prepare six replicate samples and obtain the following peak areas for your analyte:

106.5
104.2
103.7
107.1
99.2
104.7

The values are fairly close together, but what about 99.2? Should this data point be rejected or kept? i.e. Is it a legitimate data point or an outlier? To answer this, we use a test to determine — at a defined confidence limit — the reliability of the data point, known as the Q Test.

We need to compute two values, Q_calculated and Q_table. Q_calculated is obtained as follows: Q_calculated = gap / range

...where gap is the absolute difference between the suspect data point and its nearest neighbor and range is the difference between the highest and lowest values in the data set.

gap = 103.7 – 99.2 = 4.5
range = 107.1 – 99.2 = 7.9
Q_calculated = 4.5 / 7.9 = 0.57

To find Q_table, we look it up in the following table:

We have 6 data points, so at the 95% confidence level, Q_table= 0.625. The criteria for acceptance or rejection are as follows:

If Q_calculated< Q_table, accept the data point
If Q_calculated> Q_table, reject the data point

Since 0.57< 0.625, the data point can be kept with confidence.

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