HiI am testing that data are normally distributed by using

[Mark J.]

Hi
I am testing that data are normally distributed by using chi-square test.
I get a very high chi coefficient about 173.
This is very strange because data fits at 3sigma test and median mode are equal so I am almost sure that is normal distribution.
What I am doing wrong?
Which test do you advice for a better approach?
Regards

 

[SW VandeCarr]

Hi
I am testing that data are normally distributed by using chi-square test.
I get a very high chi coefficient about 173.
This is very strange because data fits at 3sigma test and median mode are equal so I am almost sure that is normal distribution.
What I am doing wrong?
Which test do you advice for a better approach?
Regards

The Central Chi Square will approach the normal distribution as the number of degrees of freedom gets large. For over 50 df, the distribution is effectively normal.

 

[Mark J.]

The problem is that I don't have so much degrees of freedom to get this value.
The other tests Kolmogorov or AD work just fine.
Maybe I am miscalculating degrees of freedom or 310 data are an issue?
Please help

 

[SW VandeCarr]

The problem is that I don't have so much degrees of freedom to get this value.
The other tests Kolmogorov or AD work just fine.
Maybe I am miscalculating degrees of freedom or 310 data are an issue?
Please help

Are you saying you have 310 data points? If they are independent observations that would mean you have 309 df. In this case, you certainly should be able to assume a normal distribution with the central chi square. When you say the other tests of normality work fine, do you mean they are consistent with normality? If so, you should be fine. However, I think you might be aggregating data in some way, and how you do this could be important in interpreting the data..

 

[blue_raver22]

Hello,

There are a few things to consider when testing for normal distribution using the chi-square test. First, it is important to have a large enough sample size for the test to be accurate. If your sample size is too small, the chi-square test may not be the most appropriate method for testing normality.

Additionally, the chi-square test assumes that the data is continuous and independent. If your data is not continuous or if there are dependencies among the data points, the results of the chi-square test may not be reliable.

Furthermore, the chi-square test is typically used to test for differences between observed and expected frequencies, rather than testing for normality. It may be more appropriate to use a test specifically designed for testing normality, such as the Kolmogorov-Smirnov test or the Shapiro-Wilk test.

In general, it is important to consider the assumptions and limitations of any statistical test before interpreting the results. It may also be helpful to consult with a statistician or seek out additional resources for guidance on selecting the most appropriate test for your data. Best of luck with your analysis.