I just looked more carefully at your code.
You are computing the unequal-variance (Welch) version of the t-test, so
that's why there isn't a problem. Compare it with the equal-variance
t-test, using the pooled variance estimate, which does have a problem, as
below
-thomas
tstat4 <- function() {
n1 = 7
mu1 = 100
sigma1 = 25
n2 = 14
mu2 = 100
sigma2 = 10
x1 = rnorm(n1, mu1, sigma1)
x1bar = mean(x1)
s1 = sd(x1)
x2 = rnorm(n2, mu2, sigma2)
x2bar = mean(x2)
s2 = sd(x2)
t = ((x1bar - x2bar) - (mu1 - mu2))/sqrt(s1^2/n1 + s2^2/n2)
t2= ((x1bar - x2bar) - (mu1 - mu2))/sqrt(((n1-1)*s1^2 + (n2-1)*s2^2)/(n1
+n2-2)*(1/n1+1/n2))
return(c(t,t2))
}
tstats4 = replicate(10000, tstat4())
hist(tstats4[1,], breaks = "scott", prob = TRUE, xlim = c(-4, 4), ylim = c(0
, 0.4))
x = seq(-4, 4, length = 200)
y = dt(x, df = 48)
lines(x, y, type = "l", col = "red")
hist(tstats4[2,], breaks = "scott", prob = TRUE, xlim = c(-4, 4), ylim = c(0
, 0.4))
x = seq(-4, 4, length = 200)
y = dt(x, df = 48)
lines(x, y, type = "l", col = "red")
On Thu, Apr 18, 2013 at 12:28 PM, David Arnold <dwarnol...@suddenlink.net>wrote:
> OK,although the variance ratio was already 2.25 to 1, tried sigma1=10,
> sigma2=25, which makes the ratios of the variances 6.25 to 1.
>
> Still no change. See:
>
> http://msemac.redwoods.edu/~darnold/math15/R/chapter11/DistributionForTwoIndependentSamplesPartII.html
>
> D.
>
>
>
> --
> View this message in context:
> http://r.789695.n4.nabble.com/t-statistic-for-independent-samples-tp4664553p4664556.html
> Sent from the R help mailing list archive at Nabble.com.
>
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--
Thomas Lumley
Professor of Biostatistics
University of Auckland
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