There may be a seasonal effect, so you should consider a paired test.
If the 12 paired differences
d = x.19 - d.20 are nearly normal,
then a paired t test should be OK. But of there is a Covid effect after the
first few months of 2020, data may not be normal. In that case,
perhaps data will be far from normal, and you might want to use
a nonparametric Wilcoxon signed rank test on the differences.
If the point is to illustrate a Covid effect maybe compare only
nine months April through December for the two years.
If you know that there were attendance restrictions because of
Covid, so that attendance was greatly reduced, you might not need
any statistical test to confirm the obvious.
If you want a less-speculative answer, perhaps post the data.
Fake data to illustrate:
x.19 = c(521,526,552, 560,548,436, 388,290,383, 490,524,517)
x.20 = c(532,529,650, 334,357,240, 211,205,241, 220,346,309)
d = x.19 - x.20
Min. 1st Qu. Median Mean 3rd Qu. Max.
-98.0 63.0 177.5 130.1 199.0 270.0
A boxplot shows marked skewness and a median very far from 0.
A normal probability plot is far from linear. With such a small
sample size I would not trust to the P-value of a paired t test
to be accurate.
qqnorm(d); qqline(d, col="green")
One Sample t-test
t = 4.0056, df = 11, p-value = 0.002066
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
mean of x
Wilcoxon signed rank test
V = 71, p-value = 0.009277
alternative hypothesis: true location is not equal to 0
There is a clear difference between the two years, which may not
require a test to be obvious. For what it's worth, I would prefer the nonparametric test.