Q - test: How can I find the Q critical value at 95% Confidence?

[Another]

TL;DR

I want to use Q -test to truncate the data. But I have number of data 180 ( n = 180 )

How can i find Q critical value at 95 % Confidence ,When number of data equal to 180 ?

I want to use Q -test to truncate the data. But I have number of data 180 ( n = 180 )
How can i find Q critical value at 95 % Confidence ,When number of data equal to 180 ?

 

[Dale]

I fundamentally disagree with the Q test and similar outlier rejection techniques. I tend to use graphical methods to visually assess possible outliers. For any possible outlier I look at the data and see if there was a transcription error or a recorded experimental "glitch" or flaw. If there is a transcription error then I correct it, and if there was a recorded "glitch" then I reject it. I never reject data simply based on distributional assumptions.

In particular, the Q test assumes normality. An outlier can tell you that your data is not normal. If you assume normality anyway and reject the outlier then you are ignoring important information telling you that your assumption is wrong.

That said, the Q test is only intended for small numbers of observations. 180 is too many. You should pick a different test (or use a different approach alltogether).

 

[Another]

I fundamentally disagree with the Q test and similar outlier rejection techniques. I tend to use graphical methods to visually assess possible outliers. For any possible outlier I look at the data and see if there was a transcription error or a recorded experimental "glitch" or flaw. If there is a transcription error then I correct it, and if there was a recorded "glitch" then I reject it. I never reject data simply based on distributional assumptions.

In particular, the Q test assumes normality. An outlier can tell you that your data is not normal. If you assume normality anyway and reject the outlier then you are ignoring important information telling you that your assumption is wrong.

That said, the Q test is only intended for small numbers of observations. 180 is too many. You should pick a different test (or use a different approach alltogether).

What do you think, if I try this program?
https://miniwebtool.com/outlier-calculator/

 

[Dale]

I use something similar in my graphical methods. One of the graphical methods I use is a box and whisker plot which uses this calculation to determine if any data points should be plotted individually. I can then look at those individually plotted points and see if there is a transcription error or an experimental reason to reject the data. So I do use that method, not to directly reject data, but to spot data that I should look at in more detail.

 

[Another]

I use something similar in my graphical methods. One of the graphical methods I use is a box and whisker plot which uses this calculation to determine if any data points should be plotted individually. I can then look at those individually plotted points and see if there is a transcription error or an experimental reason to reject the data. So I do use that method, not to directly reject data, but to spot data that I should look at in more detail.

When number of sample is small , we can use Q - test to select outliers data. If we find that there is still outlier data , we can use Q-test to confirm reject this data again.

When number of sample is large , I want to use Box Plot method to select outliers data.
If I find that there is still outlier data , I can use Box Plot method again ?

 

[Dale]

When number of sample is small , we can use Q - test to select outliers data. If we find that there is still outlier data , we can use Q-test to confirm reject this data again.

No. The Q test should never be used more than once and is only used to reject at most one observation.

When number of sample is large , I want to use Box Plot method to select outliers data.
If I find that there is still outlier data , I can use Box Plot method again ?

You can use Box Plots as often as you like, but I would not reject any data based only on the Box Plot itself. All that should do is identify data points to investigate. If you investigate and find that there is no transcription error and no experimental problem, then you should keep the outlier.