Why do we use Z test for proportions and why not T test. I have found a similar question here but I am unable to get what the answer tries to convey. It would be of great help if anyone could explain the reason in comparatively easy words.
As this answer says in detail, the assumptions underlying the t-test only strictly hold when the individual data values are sampled from a normal distribution.
Proportions are limited to values between 0 and 1, while values taken from a normal distribution can be any real number. And unlike a normal distribution, where the mean and variance of a sample are independent, once you know the proportion you have some information about the variance. So proportions don't meet the assumptions needed for a t-test to be valid.
As you take more and more samples, however, the distribution of average values in most practical applications comes close to a normal distribution. The z-test is based directly on the normal distribution. So although the z-test might not be exact with very few observations it doesn't take very many observations for it to be a very good approximation.