Testing if year-to-year change is significant? I have
Two related questions:
1) I have raw counts for voting vs. non-voting for the years 2018 and 2020.
2018 -> 11,000 voters and 3,000 non-voters
2020 -> 10,000 voters and 3,500 non-voters
How can I calculate that the decrease in voters from 2018 to 2020 is significant or not? What test would I use?

2) I also have the voter turnout rate which is 67% in 2018 and 66% in 2020. How can I check whether this 1% drop in voter turnout rate is significant?
what tests could I use in each case?
Thank you in advance!
 A: (1) In 2018, you have 11,000 voters out of 14,000 potentially available,
and in 2020, you have 10,000 voters out of 13,500.
In R, assuming randomness in willingness and ability to vote, you could compare the two proportions of voters
using prop.test as follows (declining continuity correction with parameter cor=F on account of large sample sizes):
prop.test(c(11000,10000), c(14000,13500), cor=F)

        2-sample test for equality 
        of proportions without 
        continuity correction

data:  c(11000, 10000) out of c(14000, 13500)
X-squared = 77.015, df = 1, p-value < 2.2e-16
alternative hypothesis: two.sided
 95 percent confidence interval:
  0.03493139 0.05501570
 sample estimates:
    prop 1    prop 2 
 0.7857143 0.7407407 

The proportions $.786$ and $.741$ are significantly
different on account of the P-value near $0.$
Alternatively, you could do a chi-squared test on
a $2\times 2$ table:
TBL = rbind(c(11000, 10000), c(3000, 3500)); TBL
      [,1]  [,2]
[1,] 11000 10000
[2,]  3000  3500

chisq.test(TBL, cor=F)

        Pearson's Chi-squared test

data:  TBL
X-squared = 77.015, df = 1, p-value < 2.2e-16

Except for the input syntax, the two tests are
essentially equivalent.
(2) You need to use counts (as above) instead of
percentages.
