Hi > On Oct 25, 2014, at 4:08 PM, Magnus Danielson <magnus@rubidium.dyndns.org> wrote: > > Bob, > > On 10/25/2014 08:54 PM, Bob Camp wrote: >> Hi >> >>> On Oct 25, 2014, at 2:18 PM, Magnus Danielson <magnus@rubidium.dyndns.org> wrote: >>> >>> >>> >>> On 10/25/2014 07:06 PM, Tom Van Baak wrote: >>>>> In the case of the TimePod, the data can be presented when you have *very* few samples to work with. >>>>> That said, it is interesting to watch it bring up error bars (which are indeed correctly calculated) >>>>> and then see the trace walk outside those error bars as the run progresses. >>>>> There are other measurements that are a bit less susceptible to this. >>>>> None of them have any magic to get around sqrt(N). >>>>> >>>>> Bob >>>> >>>> Maybe I don't understand error bars. For a dynamic display like TimeLab, walking outside (1 sigma) error bars is expected about 1/3 of the time, no? >>> >>> Error bars works a little differently, as they indicate with some probability (say 1-sigma) within which range the real value is. >>> >>> By the way, sqrt(N) is not very accurate estimator. >> >> But it is a common way to express the fact that your data is unlikely to converge any faster than sqrt(N)… > > Yes, but it gives you false hope of how quickly it really converged, as the cross-correlations make you converge even slower than sqrt(N). Except that management would really like it to converge as N … Bob > > Cheers, > Magnus > _______________________________________________ > time-nuts mailing list -- time-nuts@febo.com > To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts > and follow the instructions there.

Bob, On 10/25/2014 11:45 PM, Bob Camp wrote: > Hi > >> On Oct 25, 2014, at 4:08 PM, Magnus Danielson <magnus@rubidium.dyndns.org> wrote: >> >> Bob, >> >> On 10/25/2014 08:54 PM, Bob Camp wrote: >>> Hi >>> >>>> >>>> Error bars works a little differently, as they indicate with some probability (say 1-sigma) within which range the real value is. >>>> >>>> By the way, sqrt(N) is not very accurate estimator. >>> >>> But it is a common way to express the fact that your data is unlikely to converge any faster than sqrt(N)… >> >> Yes, but it gives you false hope of how quickly it really converged, as the cross-correlations make you converge even slower than sqrt(N). > > Except that management would really like it to converge as N … I bet they do, until you explain to them the consequence of that... with over-optimistic numbers making the product look bad when it hits reality and smearing the trust in the company and product names... There is a certain art to potty-train management. Cheers, Magnus