Just spoke to Paolo who developed the model. He thinks there is an issue that was spotted by Kevin. As he never received a copy of the User Manual for the software he cannot verify what is the problem. I will send him a copy and circulate the response.

 

Adam

 

 

 

 

 

Can anyone offer clarification of the outlier test?

 

Appendix B (3.) of the CIEH guidance states the Grubb’s Test assumes the other data values in a dataset, except for the suspect observation, are normally distributed.  It tells you to check the normality of the remaining dataset using the method in Appendix C.

 

Appendix B (4.) states that if the (remaining) dataset is non-normal, consider using a log transform and check if this is normal.

 

However, when you use the Statistics Calculator spreadsheet it appears to do something different.

 

On the outlier test sheet you have a choice of drop-down “use normal distribution to check for outliers” or “use log-normal distribution to check for outliers”. 

 

This appears to be checking for outliers based on the distribution of the whole dataset, ie including any suspect values, not on the dataset once outliers are removed.

 

Here is an example: take the following dataset and assume the critical value (SGV) is 20:-

14
9
13
19
14
14
14
11
18
18
28
38

 

If you follow the instructions for the stats calculator the summary page tells you this is a non-normal dataset, so you choose “log-normal to check for outliers” and it says there are no outliers.  As it is non-normal, the Chebychev Test is used to calculate the UCL (=27.6) which exceeds the critical value.  Lets say that means an exceedance of SGV so remediation is required for planning purposes. [Conclusion 1]

 

However, if you follow Appendix B you have to determine if the dataset is normal once suspect values have been removed.  The only way I can see to do this, apart from just visual assessment, is to choose “normal distribution to check for outliers” in the calculator.  This procedure indicates 28 and 38 in this dataset are outliers (something that you might suspect simply by looking at the values without having to use the calculator). 

 

Now here is the interesting bit.  If you remove these two outliers (lets assume they represent part of another dataset) the remaining data are normally distributed.  According to my reading of Appendix B (3.) this means that the Grubbs Test for outliers is appropriate (ie the use of the calculator’s  “normal distribution” method which removes 28 and 38 is justified).  It also means that the one-sample t-test is used to calculate the UCL (16.2) which is less than the critical value.  Lets say this means the bulk of the site does not exceed the SGV but there are potentially 2 hotspots of 28 & 38 requiring remediation. [Conclusion 2]

 

So, if you follow the calculator instructions you arrive at Conclusion 1 but if you follow Appendix B of the guidance you arrive at Conclusion 2. 

 

Am I doing something stupid, have I missed something, or is there an inconsistency between the approaches in the guidance and the calculator? 

 

Feedback would be welcomed.

 

 

 

Regards,

Kevin Privett.

 

Dr Kevin Privett

Geo-Environmental Associate

 

Hydrock Consultants Ltd

Over Court Barns

Over Lane

Almondsbury

Bristol

BS32 4DF

 

Tel: (01454) 619533

Fax: (01454) 614125

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Cell phone: (07799) 430870

 

Offices in Bristol, Plymouth, Northampton, Stoke-on-Trent.  www.hydrock.com

 

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