Dear all, I am a beginner with R (and also with the statistics) for which I hope to be clear. I should do this non-parametric test on data I extracted from maps. In practice I have a column that represents the landscape Dynamics of a certain time period (there are 3 dynamics, each of them marked by the number 1, 2 or 3) and the other column with the values of a topographic variable (for example the slope) . In all, there are more than 90,000 pairs of values. Going to do the test in R, for all the dynamics and for all the variables, I get out of the values of chi-square elevated (even in the order of thousands) and a p-value always <2.2e-16 .... why? Where can the error be? in the script or in the test approach? Thanks in advance [[alternative HTML version deleted]]
Dear all, I am a beginner with R (and also with the statistics) for which I hope to be clear. I should do this non-parametric test on data I extracted from maps. In practice I have a column that represents the landscape Dynamics of a certain time period (there are 3 dynamics, each of them marked by the number 1, 2 or 3) and the other column with the values of a topographic variable (for example the slope) . In all, there are more than 90,000 pairs of values. Going to do the test in R, for all the dynamics and for all the variables, I get out of the values of chi-square elevated (even in the order of thousands) and a p-value always <2.2e-16 .... why? Where can the error be? in the script or in the test approach? Thanks in advance [[alternative HTML version deleted]]
Dear Giuseppe If I understand you correctly you have a very large sample size so it is not surprising that you get very small p-values. Eevn a scientifically uninteresting difference can become statistically significant with large samples. You probably need to define a metric for meaningful differences between groups and calculate a confidence interval for it. Michael On 21/12/2018 15:37, Giuseppe Cillis wrote:> Dear all, > I am a beginner with R (and also with the statistics) for which I hope to > be clear. > I should do this non-parametric test on data I extracted from maps. > In practice I have a column that represents the landscape Dynamics of a > certain time period (there are 3 dynamics, each of them marked by the > number 1, 2 or 3) and the other column with the values of a topographic > variable (for example the slope) . In all, there are more than 90,000 pairs > of values. > Going to do the test in R, for all the dynamics and for all the variables, > I get out of the values of chi-square elevated (even in the order of > thousands) and a p-value always <2.2e-16 .... why? Where can the error be? in > the script or in the test approach? > Thanks in advance > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. >-- Michael http://www.dewey.myzen.co.uk/home.html
Dear Michael, Thanks for your answer. So, I'm not an expert in R and statistics, how can I create this interval of confidence of groups? Thanks Gc Il giorno sab 22 dic 2018, 13:34 Michael Dewey <lists at dewey.myzen.co.uk> ha scritto:> Dear Giuseppe > > If I understand you correctly you have a very large sample size so it is > not surprising that you get very small p-values. Eevn a scientifically > uninteresting difference can become statistically significant with large > samples. You probably need to define a metric for meaningful differences > between groups and calculate a confidence interval for it. > > Michael > > On 21/12/2018 15:37, Giuseppe Cillis wrote: > > Dear all, > > I am a beginner with R (and also with the statistics) for which I hope to > > be clear. > > I should do this non-parametric test on data I extracted from maps. > > In practice I have a column that represents the landscape Dynamics of a > > certain time period (there are 3 dynamics, each of them marked by the > > number 1, 2 or 3) and the other column with the values of a topographic > > variable (for example the slope) . In all, there are more than 90,000 > pairs > > of values. > > Going to do the test in R, for all the dynamics and for all the > variables, > > I get out of the values of chi-square elevated (even in the order of > > thousands) and a p-value always <2.2e-16 .... why? Where can the error > be? in > > the script or in the test approach? > > Thanks in advance > > > > [[alternative HTML version deleted]] > > > > ______________________________________________ > > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > > https://stat.ethz.ch/mailman/listinfo/r-help > > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > > and provide commented, minimal, self-contained, reproducible code. > > > > -- > Michael > http://www.dewey.myzen.co.uk/home.html >[[alternative HTML version deleted]]
On Fri, 21 Dec 2018 16:37:54 +0100 Giuseppe Cillis <giucillis at gmail.com> wrote:> Dear all, > I am a beginner with R (and also with the statistics) for which I > hope to be clear. > I should do this non-parametric test on data I extracted from maps. > In practice I have a column that represents the landscape Dynamics of > a certain time period (there are 3 dynamics, each of them marked by > the number 1, 2 or 3) and the other column with the values of a > topographic variable (for example the slope) . In all, there are more > than 90,000 pairs of values. > Going to do the test in R, for all the dynamics and for all the > variables, I get out of the values of chi-square elevated (even in > the order of thousands) and a p-value always <2.2e-16 .... why? Where > can the error be? in the script or in the test approach? > Thanks in advance >Your question and your problem is not about R, but rather about why your results do not match your expectations. There are a lot of questions you should be considering like, why use Kruskal-Wallis, before you even start wondering about R? JWDougherty