Interpretation about a result from 2-sample t test obtained from R studio I am trying to comparing the means of two samples and here is the output of my t test:
>t.test(Hrs_female,Hrs_male, conf.level = 0.95, alternative = "greater")
Welch Two Sample t-test
data:  Hrs_female and Hrs_male
t = -0.20031, df = 57.848, p-value = 0.579
alternative hypothesis: true difference in means is greater than 0
95 percent confidence interval:
-0.93453      Inf
sample estimates:
mean of x mean of y 
3.966667  4.066667

How do I interpret such a 95% confidence interval with an upper bound of Inf? Does it mean my sample size is not large enough?
Thank you! 
 A: You have set the argument alternative = "greater" for a one sided test. That is why either Inf or -Inf is the natural border, you requested.
 Here an example with a large number of cases:
> t.test(rnorm(20000), rnorm(20000), alternative = "less")

Welch Two Sample t-test

data:  rnorm(20000) and rnorm(20000)
t = -1.139, df = 39998, p-value =
0.1274
alternative hypothesis: true difference in means is less than 0
95 percent confidence interval:
       -Inf 0.00504815
sample estimates:
   mean of x    mean of y 
-0.008239926  0.003125650 

or, to demonstrate positive Inf:
> t.test(rnorm(20000), rnorm(20000), alternative = "greater")

Welch Two Sample t-test

data:  rnorm(20000) and rnorm(20000)
t = 1.368, df = 39998, p-value = 0.08565
alternative hypothesis: true difference in means is greater than 0
95 percent confidence interval:
 -0.002771267          Inf
sample estimates:
   mean of x    mean of y 
 0.008055759 -0.005638076 

So you see, large $n$ has nothing to do with it, only the one-sided test.
