How to obtain Critical Values by Monte Carlo
Luis A. Afonso
It´s well known that concerning Test of Hypotheses the majority of the usual sample statistics distribution laws are only approximate. This fact leads frequently to errors in the final DECISIONS because the degree of approximation is generally ignored (or simply unknown). Therefore (among other strategies) the use of the Monte Carlo simulative method (MC) is a common practice since the last FOUR DECADES.
The idea behind MC is to synthesise randomly a large amount of the total Sample Space (SS) and then to build the sample statistics in study. From this generally very large set we find out the values (critical values) concerning the cumulative frequencies we want, for example, 0.005, 0.01, 0.025, 0.975, 0.99, 0.995.
From this values we obtain the CONFIDENCE INTERVALS of the two-tailed tests relative to the probabilities 99%, 98%, 95% the parameter be inside.
It worth to be noted that the sample statistics values need not to be ordered (a very time-consuming step): it is sufficient to classify them in very narrow (typically 0.001), classes.
Such an algorithm can be easily be carry out in a common Personal Computer as long as we are able to simulate the samples.
Luis A. Afonso
Jack Tomsky
Once again, this shows confusion between sample statistics and parameters. I strongly urge that no one pay any attention to such nonsense.
Jack
John Smith
How I have missed your ignorance. Your absence deprived me of the joy of pointing out your ignorance.
You write:
"From this values we obtain the CONFIDENCE INTERVALS of the two-tailed tests relative to the probabilities 99%, 98%, 95% the parameter be inside."
Either the parameter is INSIDE or OUTSIDE. The probability that the parameter is inside is either 100% or 0%. Same for the probability that the parameter is outside.
I thank Allah that he put a moron like you on this earth so that I could be amused.
John