If that is a Q-Q plot then your data is not normal.
Tell us about the dataset.
i would like to test for special causes, but if my data is non-normal , is there any way out or I need to gather at least 30 for my sample size ?
i can't get 30 as I only have data from 2010. and the number of errors made is less than 30.
Plot also histogram of your data. You have most of your data =0 which indicates normal dist is not a candidate and perhaps a right-skewed distribution is need like chi-square ..etc. Also check Poisson distribution. Please post a table of your complete data.
This is my dataset :
Month No of Errors
Jan 2010 2
Feb 2010 3
Mar 2010 1
Apr 2010 1
May 2010 1
Jun 2010 4
Jul 2010 1
Aug 2010 1
Sep 2010 0
Oct 2010 2
Nov 2010 3
Dec 2010 1
Jan 2011 2
Feb 2011 2
Mar 2011 1
Apr 2011 0
May 2011 5
Jun 2011 0
Jul 2011 0
Aug 2011 0
Sep 2011 1
Oct 2011 0
Nov 2011 0
Dec 2011 1
Jan 2012 0
Feb 2012 0
Mar 2012 0
Apr 2012 4
May 2012 1
Jun 2012 1
Jul 2012 1
A histogram of your data indicates right-skewed data:
Normal Q-Q diagram is:
And variation of data form normal:
To test whether data fits a normal distribution or a Poisson distribution:
H0: data is normally distributed :: reject (p<0.05)
H1: data follows a Poisson distribution :: accept (p>0.05)
Data can also follow other distributions. You should check and select the best that fits and describes your data.
Please notice that at p=0.01 significance level your data can be considered marginally normally distributed but not at p=0.05 significance level. However, usually as you collect more data these conditions will change.
With Reference to
Identifying the Distribution of Your Data - Minitab
I do a check and I got this :
P<0.005 so it is not normally distributed. Where do i go from here ?