t.test in R between two normal distributions without original data I would like to calculate Student's t test of two normal distribution data. 
However, I do not have the data at all, but only mean and standard deviation of each population.
So, how can I simulate those data in R and calculate the t.test among both?
These are the values (mean and SD):
Population 1: 6,62 +- 0.52 years
Population 2: 6.31 +- 0.49 years
Thanks in advance
Mario
 A: For the t.test function, you need the actual data.
The following function, T.test, allows you to compute a t-test based on means, standard deviations, and numbers of observations:
T.test <- function(n, mean, sd) {
  s <- sum((n - 1) * sd^2) / (sum(n) - 2) # weighted variance
  t <- sqrt(prod(n) / sum(n)) * (diff(mean) / sqrt(s)) # t statistic
  df <- sum(n) - 2  # degrees of freedom
  p <- (1 - pt(abs(t), df)) * 2 # p value
  c(t = t, p = p)
}

If you do not know n, you cannot compute the t-test. For the following example, I assume that $n_1 = 10$ and $n_2 = 12$
dat <- data.frame(mean = c(6.62, 6.31), sd = c(.52, .49), n = c(10, 12))
#   mean   sd  n
# 1 6.62 0.52 10
# 2 6.31 0.49 12

T.test(dat$n, dat$mean, dat$sd)
#          t          p 
# -1.4373111  0.1660915 

The function returns the $t$ statistic and the corresponding $p$-value.
A: You can do something very like what you originally asked. 
The usual t-tests - including the Welch-Satterthwaite version - depend only on the summary statistics; mean, standard deviation and sample size (even the paired test depends depends on those summary statistics -- for the pair-differences).
So you can simulate data -- the distribution doesn't matter -- as long as it has the right mean, standard deviation and sample size. (Your example doesn't give the same sizes!)
I'm going to illustrate with two examples where I'll assume n1=10 and n2=12 but using your mean and variance:
# example: 
# step 0: set up summary statistics
m1=6.62; s1=0.52; n1=10
m2=6.31; s2=0.49; n2=12

# method 1: normal samples, rescaled to match original:
z1 = rnorm(n1)
x1 = scale(z1)*s1+m1
z2 = rnorm(n2)
x2 = scale(z2)*s2+m2
t.test(x1,x2) # Welch-Satterthwaite
t.test(x1,x2,var.equal=TRUE) # equal variance

# distribution 2: uniform samples, rescaled to match original:
z1 = runif(n1)
x1 = scale(z1)*s1+m1
z2 = runif(n2)
x2 = scale(z2)*s2+m2
t.test(x1,x2) # Welch-Satterthwaite
t.test(x1,x2,var.equal=TRUE) # equal variance

Summarizing the results from that:
Distrib.    t-test  
Sampled     type       t-stat   df        p

Normal      Welch      1.4292  18.817   0.1693
Uniform     Welch      1.4292  18.817   0.1693

Normal      Eq.Var     1.4373   20      0.1661
Uniform     Eq.Var     1.4373   20      0.1661

So as you suggest in your question, you can simulate; if your simulated values are in z1, then in R, x1 = scale(z1)*s1+m1 (where m1 and s1 are the required sample mean and variance) gives you a sample with the summary stats you need. (Note that scale by default standardizes for the sample mean and sd).
Similar tricks would work in most packages.
