Robert Hall
2009-Sep-16 18:49 UTC
[R] T-test to check equality, unable to interpret the results.
Hi, I have the precision values of a system on two different data sets. The snippets of these results are as shown: sample1: (total 194 samples) 0.6000000238 0.8000000119 0.6000000238 0.2000000030 0.6000000238 ... ... sample2: (total 188 samples) 0.80000001 0.20000000 0.80000001 0.00000000 0.80000001 0.40000001 ... ... I want to check if these results are statistically significant? Intuitively, the similarity in the two results mean the results are statistically significant. I am using the t-test t.test(sample1,sample2)to check for similarity amongst the two results. I get the following output: ----------------------------------------------- Welch Two Sample t-test data: s1p5 and s2p5 t = 0.9778, df = 374.904, p-value = 0.3288 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.03170059 0.09441172 sample estimates: mean of x mean of y 0.5138298 0.4824742 ------------------------------------------------ I believe the t-test checks for difference amongst the two sets, and p-value < 0.05 means both thesets are statistically different. Here while checking for dissimilarity the p-value is 0.3288, does it mean that higher the p-value (while t.test checks for dis-similarity) means more similar the results are (which is the case above as the means of the results are very close!) Please help me interpret the results.. thanks in advance! -- Rob Hall Masters Student ANU [[alternative HTML version deleted]]
Robert Hall
2009-Sep-16 18:55 UTC
[R] T-test to check equality, unable to interpret the results.
Hi, I have the precision values of a system on two different data sets. The snippets of these results are as shown: sample1: (total 194 samples) 0.6000000238 0.8000000119 0.6000000238 0.2000000030 0.6000000238 ... ... sample2: (total 188 samples) 0.80000001 0.20000000 0.80000001 0.00000000 0.80000001 0.40000001 ... ... I want to check if these results are statistically significant? Intuitively, the similarity in the two results mean the results are statistically significant. I am using the t-test t.test(sample1,sample2)to check for similarity amongst the two results. I get the following output: ----------------------------------------------- Welch Two Sample t-test data: s1p5 and s2p5 t = 0.9778, df = 374.904, p-value = 0.3288 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.03170059 0.09441172 sample estimates: mean of x mean of y 0.5138298 0.4824742 ------------------------------------------------ I believe the t-test checks for difference amongst the two sets, and p-value < 0.05 means both thesets are statistically different. Here while checking for dissimilarity the p-value is 0.3288, does it mean that higher the p-value (while t.test checks for dis-similarity) means more similar the results are (which is the case above as the means of the results are very close!) Please help me interpret the results.. thanks in advance! -- Rob Hall Masters Student ANU [[alternative HTML version deleted]]
Greg Snow
2009-Sep-18 15:38 UTC
[R] T-test to check equality, unable to interpret the results.
It appears that you have a fundamental misunderstanding of what p-values do and do not say (though this misunderstanding is commom). The following article addresses this issue and could help with a better understanding: Murdock, D, Tsai, Y, and Adcock, J (2008) _P-Values are Random Variables_. The American Statistician. (62) 242-245. See also the Pvalue.norm.sim function in the TeachingDemos package for simulation examples demonstrating points from the article. Hope this helps, -- Gregory (Greg) L. Snow Ph.D. Statistical Data Center Intermountain Healthcare greg.snow at imail.org 801.408.8111> -----Original Message----- > From: r-help-bounces at r-project.org [mailto:r-help-bounces at r- > project.org] On Behalf Of Robert Hall > Sent: Wednesday, September 16, 2009 12:55 PM > To: r-help > Subject: [R] T-test to check equality, unable to interpret the results. > > Hi, > I have the precision values of a system on two different data sets. > The snippets of these results are as shown: > > sample1: (total 194 samples) > 0.6000000238 > 0.8000000119 > 0.6000000238 > 0.2000000030 > 0.6000000238 > ... > ... > > sample2: (total 188 samples) > 0.80000001 > 0.20000000 > 0.80000001 > 0.00000000 > 0.80000001 > 0.40000001 > ... > ... > > I want to check if these results are statistically significant? > Intuitively, > the similarity in the two results mean the results are statistically > significant. > I am using the t-test t.test(sample1,sample2)to check for similarity > amongst > the two results. > I get the following output: > > ----------------------------------------------- > Welch Two Sample t-test > > data: s1p5 and s2p5 > t = 0.9778, df = 374.904, p-value = 0.3288 > alternative hypothesis: true difference in means is not equal to 0 > 95 percent confidence interval: > -0.03170059 0.09441172 > sample estimates: > mean of x mean of y > 0.5138298 0.4824742 > ------------------------------------------------ > > I believe the t-test checks for difference amongst the two sets, and p- > value > < 0.05 means both thesets are statistically different. Here while > checking > for dissimilarity the p-value is 0.3288, does it mean that higher the > p-value (while t.test checks for dis-similarity) means more similar the > results are (which is the case above as the means of the results are > very > close!) > Please help me interpret the results.. > thanks in advance! > > -- > Rob Hall > Masters Student > ANU > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help at r-project.org mailing list > 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.
Possibly Parallel Threads
- problem using KLdiv - flexmix
- Confidence intervals for Sen slope in zyp-package
- Interpreting coefficients in linear models with interaction terms
- Accounting for overdispersion in a mixed-effect model with a proportion response variable and categorical explanatory variables.
- same model, different coefficients