# Se and se_mean

when we use fit<-stan(),after use summary(fit) we can see the se_mean。Now I have some questions about the se_mean and se of statistic.What is the difference between the standard error in statistical significance and the se_mean here? How do I calculate the standard error after using the Stan function to estimate the model parameters

I also want to know what `se_mean` is . I found a page for details . Note that I cannot understand it :’-D.

I guess that the `se_mean` is the standard deviation of the posterior means. However, according to the page, it seems to me that the calculation is done using efficient samples in some sense.

In the following, I made a code to extract posterior means for three chains and calculates the standard deviation of posterior means but it did not coincides with the `se_mean`.

``````stanmodelcode <- "
data {
int<lower=0> N;
real y[N];
}

parameters {
real mu;
}

model {
target += normal_lpdf(mu | 0, 10);
target += normal_lpdf(y  | mu, 1);
}
"

y <- rnorm(20)
dat <- list(N = 20, y = y);
fit <- stan(model_code = stanmodelcode, model_name = "example",
data = dat, iter = 2012, chains = 3, verbose = F,
sample_file = file.path(tempdir(), 'norm.csv'))

print(fit,digits_summary = 5)

mu <- extract(fit)\$mu
mean(mu)
sd(mu)

a <- get_posterior_mean(fit)
x <- c(a[1,1],a[1,2],a[1,3])
sd(x)
sqrt(var(x)/length(x))
sqrt(var(c(a[1,1],a[1,2],a[1,3],a[1,4]))/4)``````

I always thought it’s standard error of the mean (SEM). If yes, you can google about it – it’s just another type of statistics for reporting