Hypothesis testing of my usage data I have usage data for the first month of operation of my music business. In this user can listen to traditional songs in diff vernacular languages. I see that 30% of user accessing my business from web client bought a subscription. Where as only 12% of user accessing my business via our app bought subscription. To plan the next quarter of development and to choose how to spend time on web vs app.
I wish to know if 30% vs 12 % difference is significant or not? How can I perform hypothesis testing on this? I am from computational background then stats….Can someone guide me how I can approach this?
EDIT:
Adding Numbers:
Total users using web = 217
Total users using web who bought subscription = 65
Total users using app = 421
Total users using app who bought subscription = 50

To test $H_0: p_w =p_a$ against $H_a: p_w \ne p_a$ you can use prop.test in R as shown below: Sample proportions are significantly different
at the 5% level of significance because the P-value is near $0.$ Also note that the 95% CI $(0.115, 0.261)$ for the difference does not include $0.$
    prop.test(c(65,50), c(212,421))

         2-sample test for equality of proportions 
         with continuity correction

data:  c(65, 50) out of c(212, 421)
X-squared = 32.211, df = 1, p-value = 1.383e-08
alternative hypothesis: two.sided
95 percent confidence interval:
 0.1149583 0.2607196
sample estimates:
   prop 1    prop 2 
0.3066038 0.1187648 

Note: Several styles of tests for comparing two
proportions are in common use. This one uses a normal approximation and is equivalent to doing a chi-squared
test on a $2 \times 2$ table with rows for Yes/No and
columns for Web/App. The P-value is the same as for prop.test, but not as much detail is given in the output.
If this is for a class, you should use the style of test
in your text or class notes, and make sure you understand the details of it.
yes = c(65, 50)
tot = c(212, 421)
no = tot - yes; no
[1] 147 371
TAB = rbind(yes, no);  TAB
    [,1] [,2]
yes   65   50
no   147  371

chisq.test(TAB)

        Pearson's Chi-squared test 
        with Yates' continuity correction

data:  TAB
X-squared = 32.211, df = 1, p-value = 1.383e-08

