Statistical test: which party do most of the voters belong to? I am looking for a statistical test able to explain the results I have found.
I have the following fields (political categories):
Right
Center
Left

I would like to see which category is mostly associated with people who voted.
My dataset is
Voted? Political Category
1            Right
0            Left
1            Center
1            Right
1            Right
1            Right

1 means that a person voted.
Most people are from Right wing. I would like to justify this statistically.
Do you know which test would be useful (e.g., correlation, chi squared, ... ) ?
Currently I have a bar chart that shows the distribution of people who voted/not voted by Political category.
I have
(1, Right) : 1500 people
(0, Right) : 202 people
(1, Left): 826 people
(0, Left): 652 people
(1, Center): 431 people
(0, Center): 542 people

Feel free to change the tags appropriately, if you need.

Data below goes into a table TBL.
(1, Right) : 1500 people
(0, Right) : 202 people
(1, Left): 826 people
(0, Left): 652 people
(1, Center): 431 people
(0, Center): 542 people

In R, as shown
below, we use Rows yes and no for voted or not, and columns R, L, and C for political views.
yes = c(1500, 826, 431)
no  = c(212, 652, 542)
TBL = rbind(yes, no);  TBL

    [,1] [,2] [,3]
yes 1500  826  431
no   212  652  542

With P-value near $0$ the chi-squared test
strongly rejects the null hypothesis that the voting and political categories are independent.
chisq.test(TBL, cor=F)

        Pearson's Chi-squared test

data:  TBL
X-squared = 630.08, df = 2, p-value < 2.2e-16

The roughly equivalent prop.test gives about
the same P-value as chisq.test (depending partly on whether
a continuity correction is used). However, this
test displays the proportions of those voting
in each political category.
tot = yes + no
prop.test(yes, tot, cor=F)

    3-sample test for equality of proportions
    without continuity correction

data:  yes out of tot
X-squared = 630.08, df = 2, p-value < 2.2e-16
alternative hypothesis: two.sided
sample estimates:
   prop 1    prop 2    prop 3 
0.8761682 0.5588633 0.4429599 

The pattern is pretty clear. However, with
appropriate adjustments to avoid 'false discovery'
by multiple analyses of the same data (perhaps
consider the Bonferroni method), post hoc tests
could be done to see whether proportions for L and C are statistically independent, and similarly for R and L (perhaps testing both at the 1% level).
