tag:blogger.com,1999:blog-21831384.post114618858597708652..comments2019-10-03T14:11:35.530+05:30Comments on BzST | Business Analytics, Statistics, Teaching: p-values in LARGE datasetsGalit Shmuelihttp://www.blogger.com/profile/06119270323184007583noreply@blogger.comBlogger4125tag:blogger.com,1999:blog-21831384.post-1146249823117989002006-04-29T00:13:00.000+05:302006-04-29T00:13:00.000+05:30Thanks Corey. The "magic" number can also be found...Thanks Corey. The "magic" number can also be found by deciding what a practical significance is. Then you can compute the sample size that would detect differences of (at least) that magnitude. <BR/><BR/>This "power computation" gives you the minimal sample size that is needed to detect an effect of the given magnitude. It will give you an idea of the magnitude of n that gives "reasonable" Galit Shmuelihttps://www.blogger.com/profile/06119270323184007583noreply@blogger.comtag:blogger.com,1999:blog-21831384.post-1146235387820184752006-04-28T20:13:00.000+05:302006-04-28T20:13:00.000+05:30Thanks for posting my question. Your explanation ...Thanks for posting my question. Your explanation was very practical and also very informative - even for us non-stats-gurus. I also like your follow up question. So do I nix 90k cases in this wonderful 120k dataset? If so, what is the magic number ...i.e do I keep culling until I finally reach non-significance? Just for argument sake, I did run only 30k cases (random sample) and I still get Anonymoushttps://www.blogger.com/profile/01765271993038255594noreply@blogger.comtag:blogger.com,1999:blog-21831384.post-1146235085347137012006-04-28T20:08:00.000+05:302006-04-28T20:08:00.000+05:30Thanks for the terrific response and example. Inde...Thanks for the terrific response and example. Indeed the "statistical vs. practical significance" is an age-old issue. I think that your angle used to be the more popular one, and at least then you could recommend the collection of additional data to increase statistical power. These days we have TOO MUCH data. And then do we recommend getting rid of some data? (:Galit Shmuelihttps://www.blogger.com/profile/06119270323184007583noreply@blogger.comtag:blogger.com,1999:blog-21831384.post-1146234483037123072006-04-28T19:58:00.000+05:302006-04-28T19:58:00.000+05:30Very nice article! The general idea of "practical ...Very nice article! The general idea of "practical relevance" versus "statistical significance" works actually also the other way round: There could be influences which are not significant in a statistical sense because the variance is too high but which are practically relevant. I had such a case in a simulation purification plant experiment once. A look at some boxplots did reveal a visible Anonymousnoreply@blogger.com