The definition of a statistic that I learned in grad school is that it's a function of a random sample from a population. Any p-value would fit that definition.
Andy > From: Adaikalavan Ramasamy > > If my usage is wrong please correct me. Thank you. > > Here are my reason : > > 1. p-value is a (cumulative) probability and always ranges > from 0 to 1. > A test statistic depending on its definition can wider range > of possible > values. > > 2. A test statistics is one that is calculated from the data > without the > need of assuming a null distribution. Whereas to calculate > p-values, you > need to assume a null distribution or estimate it empirically using > permutation techniques. > > 3. The directionality of a test statistics may be ignored. > For example a > t-statistics of -5 and 5 are equally interesting in a > two-sided testing. > But the smaller the p-value, more evidence against the null > hypothesis. > > Regards, Adai > > > > On Thu, 2005-11-10 at 06:05 -0500, Duncan Murdoch wrote: > > On 11/9/2005 10:01 PM, Adaikalavan Ramasamy wrote: > > > I think an alternative is to use a p-value from F > distribution. Even > > > tough it is not a statistics, it is much easier to > explain and popular > > > than 1/F. Better yet to report the confidence intervals. > > > > Just curious about your usage: why do you say a p-value is > not a statistic? > > > > Duncan Murdoch > > > > > > > > Regards, Adai > > > > > > > > > > > > On Wed, 2005-11-09 at 17:09 -0600, Mike Miller wrote: > > > > > >>On Wed, 9 Nov 2005, Gao Fay wrote: > > >> > > >> > > >>>Hi there, > > >>> > > >>>Suppose mu is constant, and error is normally > distributed with mean 0 and > > >>>fixed variance s. I need to find a statistics that: > > >>>Y_i = mu + beta1* I1_i beta2*I2_i + beta3*I1_i*I2_i + > +error, where I_i is 1 > > >>>Y_i is from group A, and 0 if Y_i is from group B. > > >>> > > >>>It is large when beta1=beta2=0 > > >>>It is small when beta1 and/or beta2 is not equal to 0 > > >>> > > >>>How can I find it by R? Thank you very much for your time. > > >> > > >> > > >>That's a funny question. Usually we want a statistic > that is small when > > >>beta1=beta2=0 and large otherwise. > > >> > > >>Why not compute the usual F statistic for the null > beta1=beta2=0 and then > > >>use 1/F as your statistic? > > >> > > >>Mike > > >> > > >>______________________________________________ > > >>[email protected] mailing list > > >>https://stat.ethz.ch/mailman/listinfo/r-help > > >>PLEASE do read the posting guide! > http://www.R-project.org/posting-guide.html > > >> > > > > > > > > > > ______________________________________________ > > > [email protected] mailing list > > > https://stat.ethz.ch/mailman/listinfo/r-help > > > PLEASE do read the posting guide! > http://www.R-project.org/posting-guide.html > > > > > > > ______________________________________________ > [email protected] mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! > http://www.R-project.org/posting-guide.html > > ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
