t-test on highly skewed data I have a data set with tens of thousands of observations of medical cost data.  This data is highly skewed to the right and has a lot of zeros.  It looks like this for two sets of people (in this case two age bands with > 3000 obs each):
     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
     0.0      0.0      0.0   4536.0    302.6 395300.0 
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
     0.0      0.0      0.0   4964.0    423.8 721700.0 

If I perform Welch's t-test on this data I get a result back:
Welch Two Sample t-test
    
data:  x and y
t = -0.4777, df = 3366.488, p-value = 0.6329
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -2185.896  1329.358
sample estimates:
mean of x mean of y 
 4536.186  4964.455 

I know its not correct to use a t-test on this data since its so badly non-normal.  However, if I use a permutation test for the difference of the means, I get nearly the same p-value all the time (and it gets closer with more iterations).
Using perm package in R and permTS with exact Monte Carlo
    Exact Permutation Test Estimated by Monte Carlo
    
data:  x and y
p-value = 0.6188
alternative hypothesis: true mean x - mean y is not equal to 0
sample estimates:
mean x - mean y 
      -428.2691 
    
p-value estimated from 500 Monte Carlo replications
99 percent confidence interval on p-value:
 0.5117552 0.7277040 

Why is the permutation test statistic coming out so close to the t.test value?  If I take logs of the data then I get a t.test p-value of 0.28 and the same from the permutation test.  I thought the t-test values wold be more garbage than what I am getting here.  This is true of many other data sets I have like this and am wondering why the t-test appears to be working when it shouldn't.
My concern here is that the individual costs are not i.i.d.  There are many sub-groups of people with very different cost distributions (women vs men, chronic conditions etc) that seem to violate the iid requirement for central limit theorem, or should I not worry about that?
 A: When n is large (like 300, even far less than 3000), the t-test is essentially the same as the z-test.  That is, the t-test becomes nothing more than an application of the central limit theorem, which says that the MEAN for each of your two groups is almost exactly normally distributed (even if the observations underlying the two means are very far from being normally distributed!).  This is also the reason that your typical t-table does not bother to show values for n greater than 1000 (for example, this t-table). Thus, I am not surprised to see that you are getting such well-behaved results.
Edit:  I seem to have underestimated the extremity of the skewness and its importance.  While my point above has merit in less extreme circumstances, whuber's answer to the question is much better overall.
A: Neither the t-test nor the permutation test have much power to identify a difference in means between two such extraordinarily skewed distributions.  Thus they both give anodyne p-values indicating no significance at all.  The issue is not that they seem to agree; it is that because they have a hard time detecting any difference at all, they simply cannot disagree!

For some intuition, consider what would happen if a change in a single value occurred in one dataset.  Suppose that the maximum of 721,700 had not occurred in the second data set, for instance.  The mean would have dropped by approximately 721700/3000, which is about 240.  Yet the difference in the means is only 4964-4536 = 438, not even twice as big.  That suggests (although it does not prove) that any comparison of the means would not find the difference significant.
We can verify, though, that the t-test is not applicable.  Let's generate some datasets with the same statistical characteristics as these.  To do so I have created mixtures in which


*

*$5/8$ of the data are zeros in any case.

*The remaining data have a lognormal distribution.

*The parameters of that distribution are arranged to reproduce the observed means and third quartiles.


It turns out in these simulations that the maximum values are not far from the reported maxima, either.
Let's replicate the first dataset 10,000 times and track its mean.  (The results will be almost the same when we do this for the second dataset.)  The histogram of these means estimates the sampling distribution of the mean.  The t-test is valid when this distribution is approximately Normal; the extent to which it deviates from Normality indicates the extent to which the Student t distribution will err.  So, for reference, I have also drawn (in red) the PDF of the Normal distribution fit to these results.

We can't see much detail because there are some whopping big outliers.  (That's a manifestation of this sensitivity of the means I mentioned.)  There are 123 of them--1.23%--above 10,000.  Let's focus on the rest so we can see the detail and because these outliers may result from the assumed lognormality of the distribution, which is not necessarily the case for the original dataset.

That is still strongly skewed and deviates visibly from the Normal approximation, providing sufficient explanation for the phenomena recounted in the question.  It also gives us a sense of how large a difference in means could be detected by a test: it would have to be around 3000 or more to appear significant.  Conversely, the actual difference of 428 might be detected provided you had approximately $(3000/428)^2 = 50$ times as much data (in each group).  Given 50 times as much data, I estimate the power to detect this difference at a significance level of 5% would be around 0.4 (which is not good, but at least you would have a chance).

Here is the R code that produced these figures.
#
# Generate positive random values with a median of 0, given Q3,
# and given mean. Make a proportion 1-e of them true zeros.
#
rskew <- function(n, x.mean, x.q3, e=3/8) {
  beta <- qnorm(1 - (1/4)/e)
  gamma <- 2*(log(x.q3) - log(x.mean/e))
  sigma <- sqrt(beta^2 - gamma) + beta
  mu <- log(x.mean/e) - sigma^2/2
  m <- floor(n * e)
  c(exp(rnorm(m, mu, sigma)), rep(0, n-m))
}
#
# See how closely the summary statistics are reproduced.
# (The quartiles will be close; the maxima not too far off;
# the means may differ a lot, though.)
#
set.seed(23)
x <- rskew(3300, 4536, 302.6)
y <- rskew(3400, 4964, 423.8)
summary(x)
summary(y)
#
# Estimate the sampling distribution of the mean.
#
set.seed(17)
sim.x <- replicate(10^4, mean(rskew(3367, 4536, 302.6)))
hist(sim.x, freq=FALSE, ylim=c(0, dnorm(0, sd=sd(sim.x))))
curve(dnorm(x, mean(sim.x), sd(sim.x)), add=TRUE, col="Red")
hist(sim.x[sim.x < 10000], xlab="x", freq=FALSE)
curve(dnorm(x, mean(sim.x), sd(sim.x)), add=TRUE, col="Red")
#
# Can a t-test detect a difference with more data?
#
set.seed(23)
n.factor <- 50
z <- replicate(10^3, {
  x <- rskew(3300*n.factor, 4536, 302.6)
  y <- rskew(3400*n.factor, 4964, 423.8)
  t.test(x,y)$p.value
})
hist(z)
mean(z < .05) # The estimated power at a 5% significance level

A: I know this answer is way late. However, I am getting a PhD in health services research, so I work with healthcare data a lot, including cost data.
I don't know what data the OP had. If it were cross-sectional data, then chances are it was justifiably IID. Independence means that each unit, so each person, is independent. That is very likely justifiable. As to identically distributed, the data can be modeled as all coming from, say, a gamma distribution in a generalized linear model with a log link. This is what people commonly do in practice. Or if you want to get fancy, there are probably hurdle models (popular in econometrics) that deal with the excess 0s. Which, by the way, are pretty common in healthcare spending. The OP is technically correct that the data aren't necessarily identically distributed, e.g. the mean and variance will shift with age, but it's a workable assumption in multiple regression models.
If each person was in the dataset for more than one year, then the data wouldn't be IID. There are more complex models available for that. A relatively simple one of those would probably be generalized estimating equations, gamma distribution and log link again, assume an exchangeable working correlation. Or, if these data are from publicly available survey data, there is NOT an equal probability of being sampled - many of those surveys interview multiple people in each household, and they also stratify the population and oversample some groups (e.g. racial minorities). The user would have to correct for that.
I don't use t-tests, especially not for observational data. There are too many confounders, so you would want to adjust for them in a (generalized) linear model. So, I can't comment on the questions related specifically to t-tests. 
