Isn't it amazing that some results do not require a p-value, but reviewers require one? I recall a paper I submitted some time ago in which the sample had 100% response. You didn't need a stats test to show anything, the reviewers required that I run an appropriate stats test and provide the p-value! I was not surprised, So I did chi square as I recall, it was pointless.
On Thu, July 19, 2007 3:47 am, William Silvert wrote: > Although this is really a religious rather than scientific debate which is > unlikely to lead to any concensus, I want to respond to some of Jim > Roper's > comments. > > The fact that you can learn a lot by looking at plots does not mean the > that > "results are so glaringly obvious". Humans are very good at pattern > recognition and often can see what is present in a plot better than they > can > analyse numerical data. Also, plots often show unexpected features which > lead to new knowledge - they are not just for hypothesis testing. > > On several occasions I have been consulted by people who are quite expert > at > statistics who cannot interpret their data, and who were surprised that by > plotting the results in the right way a clear answer leaped out at them. > Of > course they then had to confirm the results with statistics, but that is > mainly to get the paper past referees. > > Jim ends with the usual comment that if the statistics are carried out by > someone who is really good at stats, the results will be good. That may be > true, but good statisticians are pretty rare beasts, and in the average > lab > the plotting method is just as reliable as textbook stats. Some of you may > recall a post of mine a couple of years ago where I surveyed a lot of > statistically sophisticated fisheries scientists to see if they could add > two numbers (what is 100+-3 + 100 +-4?) and only one person came up with > the > answer - but he was very unsure of himself, and couldn't figure out how to > multiply the numbers. > > Just a glance through any journal will quickly show that most biologists > have little idea of significance and represent their results with > exaggerated precision. In a perfect world maybe we could trust all > statistical analyses, but we ain't there yet. > > Bill Silvert > > > ----- Original Message ----- > From: "James J. Roper" <[EMAIL PROTECTED]> > To: <ECOLOG-L@LISTSERV.UMD.EDU> > Sent: Wednesday, July 18, 2007 3:43 PM > Subject: Re: ECOLOGY Mathematics and the metamathematics of evasive > ecology? > Re: Request: Data sets for biocalculus project > > >> Mattheus, >> >> You are showing some misunderstanding of the use of statistics. A few >> observations. >> >> 1. If your results are so glaringly obvious, then the question was >> probably not very interesting, or a logical consequence of the methods. >> >> 2. Questions that are not so simple need statistics to discover the >> probability of something happening when it is not obligatory that it >> happen. >> >>> statistical tests when you can simply draw a plot and >>> your conclusion comes? >> 3. A plot can mislead. >>> I need to learn that populations must >>> be normal, they must be homoscedastic, there are at >>> least 3 models for ANOVA, there is something out there >>> with the name of ANCOVA, and I have no single idea if >>> this is useful for me or not... > Malcolm L. McCallum Assistant Professor of Biology Editor Herpetological Conservationa and Biology [EMAIL PROTECTED] [EMAIL PROTECTED]
