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From Phil Steitz <>
Subject Re: [math] RandomData and ValueServer Failures . . .
Date Tue, 17 Jun 2003 06:37:22 GMT

--- "Mark R. Diggory" <> wrote:
> Al Chou wrote:
> > --- "Mark R. Diggory" <> wrote:
> > 
> >>I appear to "occasionally" get JUnit test failures from ValueServer and 
> >>RandomData Tests. This would appear to be because the mean sampled 
> >>values can sometimes deviate from the expected mean even for 1000 case 
> >>draws, I know this happens "rarely", just enough over the last month or 
> >>so for me to start to notice this behavior. Oldly, when its off, its off 
> >>in a big way, so its not just a matter of changing the tolerance.
> > 
> > 
> > Approximately how big is "off in a big way"?  Is it because a pseudorandom
> > number generator is used dynamically in the test?  I guess I should look at
> the
> > test code (which I'll try to do on the train home), but offhand it
> surprises me
> > that the tests are ever far off from their expected results.
> > 
> Greater than the tolerance for the tests, which is surprising to me 
> because its set to be 0.1, the last time I saw it fail the value was 
> approx 5.1xxxxxxxx. Remember, this probibly doesn't happen very often. 
> It would be interesting to run a batch test and actually get an estimate.
> assertEquals("mean", 5.069831575018909, stats.getMean(), tolerance);
> What one has to consider, is that stats.getMean() is a sample mean that 
> can "vary" about the range of variance for the sampled values. Rarely, 
> the sample is of poor enough quality not to effectively describe the 
> populations mean and variance. There is always this small probiblity 
> that the mean of the sample will not match the mean of the values. So 
> testing with an "assertEquals" isn't vary helpfull in terms of getting 
> the mean (I remember another discussion being had earlier on the list 
> concerning having something like an assertApproximatelyEquals.
> But, what I think we could really use are statistically based assertion 
> tests!!!!!!!!!!!!!!!! :-)
> double[] population;
> double tolerance = 0.05;
> assertStudentsT("mean", population, stats, tolerance);
> then the test would be more like a t-test of sorts. Testing if the 
> sampled set of values is "significantly different" than the population 
> set. I get the feeling that this would be a stronger assertion than 
> testing if the means and standard deviations are equal.
> Any Ideas?
> -Mark

There are two concepts mixed up above.  The first one has to do with testing
randomly generated data for conformity to expected distributions.  The current
Junit test cases for RandomData and ValueServer actually do that using
statistical methods.  Specifically, most of the RandomData and ValueServer
tests use chi-square tests to compare the observed data to what would be
expected under the hypothesis that the data actually come from the advertised
distributions.  Like all statistical significance tests, these tests have a
"significance level", which is generally set at .001.  In this context, what
that means is that the tests will "randomly fail" with probability .001, even
if there is nothing wrong with the code.  That is why the Junit messages have a
disclaimer saying something like "will fail approximately 1 in 1000 times. 
Repeated failures indicate a problem".  If you look at the test cases, you will
notice that the tests for generating data from non-Uniform continuous
distributions do not use this type of test.  I think there is a FIXME somewhere
indicating that once we have t-test capabilites (which is now) we should
replace the "absolute" test with an actual t-test.  That might be a good idea;
but I would not place a high priority on it, since there is not much going on
in these methods.  If we do this, we should make sure to set the significance
level to .001 to be consistent with the other tests and to control the
incidence of "false" failures.


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