I've read that the chi square test is useful to see if a sample is significantly different from a set of expected values.
For example, here is a table of results of a survey regarding people's favourite colours (n=15+13+10+17=55 total respondents):
A chi square test can tell me if this sample is significantly different from the null hypothesis of equal probability of people liking each colour.
Question: Can the test be run on the proportions of total respondents who like a certain colour? Like below:
Where, of course, 0.273+0.236+0.182+0.309=1.
If the chi square test is not suitable in this case, what test would be? Thanks!
Edit: I tried @Roman Luštrik answer below, and got the following output, why am I not getting a p-value and why does R say "Chi-squared approximation may be incorrect"?
> chisq.test(c(0,0,0,8,6,2,0,0),p = c(0.406197174,0.088746395,0.025193306,0.42041479,0.03192905,0.018328576,0.009190708,0)) Chi-squared test for given probabilities data: c(0, 0, 0, 8, 6, 2, 0, 0) X-squared = NaN, df = 7, p-value = NA Warning message: In chisq.test(c(0, 0, 0, 8, 6, 2, 0, 0), p = c(0.406197174, 0.088746395, : Chi-squared approximation may be incorrect