R中此二项式检验中的样本估计值是什么意思？

Question

What is meant by sample estimates in this binom test. They don't seem to change with change in porbability of success . I have tried to find it's meaning in documentation and on google but can't see it. I also tried to hand compute it, to check if it meant any thing but still I cannot see what it really means. Any idea?

binom.test(60, 300, 0.3)

    Exact binomial test

data:  60 and 300
number of successes = 60, number of trials = 300, p-value = 0.0001137
alternative hypothesis: true probability of success is not equal to 0.3
95 percent confidence interval:
 0.1562313 0.2498044
sample estimates:
probability of success 
               0.2 

binom.test(60, 300, 1/6)

    Exact binomial test

data:  60 and 300
number of successes = 60, number of trials = 300, p-value = 0.1216
alternative hypothesis: true probability of success is not equal to     0.1666667
95 percent confidence interval:
0.1562313 0.2498044
sample estimates:
probability of success 
                0.2 

binom.test(60, 300, 0.5)

    Exact binomial test

data:  60 and 300
number of successes = 60, number of trials = 300, p-value < 2.2e-16
alternative hypothesis: true probability of success is not equal to 0.5
95 percent confidence interval:
 0.1562313 0.2498044
sample estimates:
probability of success 
                 0.2 

Answer 1

I will use your second chunk of code to explain (it is the same for all).

Imagine rolling a die. The probability of rolling a 6 if the die is fair is 1/6 . This is the third argument of the binom.test function. Therefore, in your example, the successes that you would expect would be 300 / 6 = 50 . This implies a (hypothesized) probability of success of 1/6 .

However, you observed 60 successes. These 60 observed successes are used to calculate the success sample estimate ie the value that you see at the bottom. This is calculated as 60 / 300 = 0.2 .

The binomial test then is used to test whether the proportion of 6s that you observed is significantly higher than would be expected by chance (ie 50 if the die is fair).