Timeline for Is ERNIE output skewed by statistical tests?
Current License: CC BY-SA 2.5
6 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Aug 28, 2010 at 22:58 | comment | added | JBL | Aaron, I don't think we disagree much (if at all). I just intended to point out that if the test being applied alters, say, every one in 1000! draws but does so in a way that destroys uniformity over the set of interest (so, not as in your example), then the result is biased, even though in the lifetime of the universe this bias will never result in any outcomes that differ from what would have happened if the selection were truly uniform. | |
| Aug 28, 2010 at 21:03 | comment | added | ABh | It also seems to depend strongly on exactly what sort of test or audit is going to be performed by the actuary to assess this RNG (random number generator). Measure the smoothness of the distribution? Cross-correlation? How many data items are in the output to be assessed? Is there absolute certainty that the machine performs the same way and utilizes exactly the same algorithm and code steps when it is creating output for testing as opposed to creating output to be an actual draw? Is the code itself audited? Has the code been verified to be correct and random by specific criteria? | |
| Aug 28, 2010 at 5:22 | comment | added | Aaron Meyerowitz | The question isn't that clearly defined. My, not that well expressed points were A) If rejection only happens on a machine malfunction or a 1 in 10^10 coincidence, then no distortion of randomness has happened in our lifetime (and any that did would be less than epsilon) B) Suppose a machine on each run generates 1000 huge numbers one after another. The machine is actually perfect but we reject a set if the numbers come out in increasing order (or with too few or too many inversions). Then we would reject valid runs but would have a uniform distribution on 1000 integer sets. | |
| Aug 28, 2010 at 0:13 | comment | added | JBL | (Of course my previous comment is based on the assumption that rejecting output actually does alter the underlying distribution.) | |
| Aug 28, 2010 at 0:12 | comment | added | JBL | "Has the output ever been rejected? If not then no bias!" This is not true -- the bias (in the underlying distribution) is still there, it's just not detectable from the data. | |
| Aug 27, 2010 at 21:43 | history | answered | Aaron Meyerowitz | CC BY-SA 2.5 |