Chi square on a huge dataset

#1
I have a situation where I want to look if a simulation gives expected values, so I think a chi squared is the way to do this. Some of the expected probabilities are quite tiny, but others are very large. After doing a chi square I noticed it gives a huge value = 867551.351! I see online that chi square is depending lots on the sample size so of course the p value is tiny and meaningless. My question is what is the correct way to look if observed and expected differ significantly, when we have such large sample and a reference distribution? Could I maybe do a log transform of the data?

Here is an example of the data which shows my problem.
Code:
class    ob    ex
1    0    10.78
2    98    97.3
3    1610    1680.7
4    10017    10087
5    14224    13755
6    181083    27475
7    133413    147896
8    301406    332773
9    2844688    2957983
10    3513461    3508239
If it is really problematic then I am not so interested in significance but would be nice to get indications of if there is a difference between observed and expected, and also WHERE are their differences.

Thanks
 

hlsmith

Less is more. Stay pure. Stay poor.
#2
So where are these data coming from. How are you simulating them and the source of the reference?

Are they supposed to be counts?