Any observation that is not within the normal trend is known as an outlier. Outlier can most of the time occur due to human error. Following is a table which provides the data for number of children in a country “A”:
The above data in Red clearly shows that these are outlier within the normal trend as it is humanly not possible to have so many children. Therefore in order to check the significance of a data which seems to be an outlier, a Grubbs test is used.
To test the seemingly out of trend / outlier data that they are significant or not. Through this test we can easily identify that an outlier is really an outlier or not. Grubbs test calculates “Z” value of the observation and then decides whether the observation or data is significantly an outlier or not.
Cut of point of Z-Value = 4.14
Z < 4.14 means insignificant (which means the data is not an outlier)
Z > 4.14 means significant (which means the data is an outlier)
You can easily check the significance of an outlier through Grubbs test by using any online available tool. One of the website that I have used mostly in my statistical analysis to test an outlier through Grubbs test is as follow:
http://graphpad.com/quickcalcs/Grubbs1.cfm
Suppose Grubbs test identifies certain data is being significantly out of trend, then what should be done next in order to achieve accurate results. Here we have three options:
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@Mikel I have gone through most of your technical posts and notice there are no comments from the site. Could you tell me if you get views from outside. You should as these are highly informative blogs.