### This is a program by Brant Balgooyen.
### Consider sampling from a normal population with mean mu = 100 and
### standard deveiation sigma = 10.  The hypothesis test H0: mu = 10 
### versus HA: mu not = 10 is tested.  If the t-test works correctly the
### Type I Error (alpha) should be 0.05.

####################################################################### 

mu_100		# population mean
sigma_10	# population standard deviation

Count_0
M_1000			# number of samples taken
alpha_.05		# size of the test
pvector_c()	# initialize a vector

for(i in 1:M) {
	x_rnorm(30,mu,sigma)
	t.output_t.test(x, mu=mu)
	pvalue_t.output$p.value
	pvector_c(pvector, pvalue)
	if (pvalue < alpha) Count_Count + 1	
}

alpha.est_Count/M
print(alpha.est)
hist(pvector)