As i am new to statistics and statistical terminology i am struggling a bit with interpreting the results of some of the things i do/have to do.
I am using RStudio as a tool for that. More precisely i am using a package nortest to perform normality distribution tests on my data. Running the Anderson-Darling normality test on my data returns A = 2946.8, p-value < 2.2e-16. A is i am guessing a statistical value of the test itself and the p-value is.. well p-value.
My question is: What does the p-value in this case signify? Is it the probability of my data sets distribution to differ from normal distribution? If yes, is it safe to assume that the distribution of the data is really-really similar to normal distribution?
Since the goal is outlier detection one of the methods proposed for this task (not by me) was a standard Z score cut off which is really sensitive to extremes in the data. The method i proposed is using the adjusted boxplot so it takes into consideration whether the data is skewed or not.
Note: The entire data set is 8702 records. Update: After all the research papers and materials i provided for my team leader, the argument against my suggestion and for using Z score is that by using the Z score it gives you something you can look at (the Z score of the observation) in comparison to the adj. boxplot where you can't observe any statistical indicator that describes the data in some way. Yea.. I know...