This is my first time answering a question, so I'm hoping that I am actually providing a useful answer.
When you run this in R:
x <- 31
n <- 50
p <- 0.75
binom.test(x, n, p = p)
... it returns the following results:
Exact binomial test
data: x and n
number of successes = 31, number of trials = 50, p-value = 0.04812
alternative hypothesis: true probability of success is not equal to 0.75
95 percent confidence interval:
probability of success
What the p-value of 0.4812 is telling you is that testing for a probability of success of 0.75 (75%) of having 31 "successful" outcomes out of 50 attempts of a binary event (0, 1) -- i.e.: flipping a coin and getting heads up 31 times out of 50 -- is just inside the 95% confidence interval, which are a range of probabilities of success.
Therefore, you could cautiously accept the null hypothesis that the probability of success is equal to 75%. (The alternative hypothesis being that the probability of success is not equal to 75%.)
The calculated probability of success is included at the bottom of the output: 0.62, or 62% chance of success. This is nothing more than 31 / 50.