Mar 21, 2011 · However, in essence they are using 0.05, but multiplied by the number of tests. It is obvious that this procedure (Bonferroni correction) can quickly lead to incredibly small p-values. That's why people in the past (in neuroscience) stopped at p<0.001. Nowadays other methods of multiple comparison corrections are used (see Markov random field ...

I'm wondering if there's a convention on which symbol, and how many of them are to be used to indicate what the significance is, e.g. * for p < 0.05 and *** for p < 0.001? statistical-significance Share

Patients with recurrent primary GBM treated with POH survived significantly longer (log rank test, P < 0.0001) than untreated group. Is it even possible to have p < 0.0001 for such a small sample & control group? I'm not a statistician, but I thought you'd need tens of thousands of samples at the very least to get that sort of confidence.

$\begingroup$ p-values tend to go down as sample size goes up. With 682k sample size you are likely to see a lot of small p-values, even the detected difference or association is trivial. With 682k sample size you are likely to see a lot of small p-values, even the detected difference or association is trivial.

Sep 15, 2020 · This should be a simple inquiry. Doing a regression analysis I found that the coefficient of a predictor has a(n) (infinitesimal) positive effect of 0.001 that is significant at the 0.005 level. I ...

As whuber mentioned, normally such p-values are reported as being less than some threshold (e.g. <0.001). One thing to be careful about is that p-values only tells you whether the difference in median is is statistically significant. Whether the difference is significant enough in magnitude is something you will have to decide: e.g. for large ...

May 29, 2020 · A p-value of 1.439e-05 equals to 1.429*10^(-5), which is less than 0.05. A Shapiro-Wilk test is the test to check the normality of the data. The null hypothesis for Shapiro-Wilk test is that your data is normal, and if the p-value of the test if less than 0.05, then you reject the null hypothesis at 5% significance and conclude that your data ...

Background One of the most commonly used weak prior on variance is the inverse-gamma with parameters $\\alpha =0.001, \\beta=0.001$ (Gelman 2006). However, this distribution has a 90%CI of approxim...

Mar 13, 2018 · r and p-value measure different things. The p-value indicates the probability of getting data as extreme† as the observed data assuming that the null hypothesis is true. By our decision rule, if p < alpha, we have sufficient evidence to reject the null hypothesis that there’s no correlation. And that’s all the p-value does for us.

Color priming effect was reliable in the nonsynesthetic F(1,22)=13.10, p<.005, np2= .37 as well as the synesthetic group F(1,22)= 24.39, p<.001, np2= .53; I have no idea what the partial eta squared means, but I have come to the understanding that if F is bigger than 1 it is significant. But then, what does the p value have to do with it?