Yes, I would compare them to {1000,1000,1000,1000,1000,1000}. Thanks for the explanation.
]]>bobbym - did you compare {900,1100,1200,1000,900,900} to {1000,1000,1000,1000,1000,1000} ? Otherwise Chi-square is 0 (and DF 0 also)
If so I get a Chi-square of 38.7332 and p = 2.68247e-7 , which is quite cool as it shows that to be a very expected result.
So, we can agree here to give them honorary categories of test1 to test6 and random vs boring.
]]>The pages Chi-Square Test and Chi-Square Calculator are well made! Thanks!
]]>Your examples work out and the page is good but I have a question:
This test only works for categorical data (data in categories), such as Gender {Men, Women} or color {Red, Yellow, Green, Blue} etc, but not numerical data such as height or weight.
I have used chi square on other than categorical data, for instance, testing the validity of a die by rolling it 6000 times and counting up the number of times 1 to 6 comes up. Say that the answer looks like this,{900,1100,1200,1000,900,900} you can calculate a p value for this to see whether or not this result could be by chance or not. So, does the quote still apply to n x 1 data? Would you consider my data as categorical?
]]>Please cast your eyes over them both, and let me know any errors or improvements so visitors get the best possible experience.
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