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A one-sided LRT is straightforward in R using the signed LRT statistic. Fit the logistic regression models with and without the $\beta$ term. Compute the ordinary LRT statistic from the deviance diffe …

In my opinion, the source that you link to is wrong in that it is confusing conditioning with assumptions. Fisher's exact test conditions on the margin totals, meaning that it does not use any informa …

The difference in differences is what is called an interaction in statistics (as Dimitriy Masterov has already pointed out). You want to test whether the time effect is different when you intervene co …

The Benjamini-Hochberg FDR calculation performed by p.adjust seems perfectly appropriate in your situation. There is no theoretical problem with tied p-values in Benjamini and Hochberg's 1995 theorem …

Yes, the approach and code that you have stated is correct, and the F-test in the output will test the null hypothesis that b1=0 and b2=1 vs the alternative that b1 and b2 are unspecified. You are tes …

That's a fair question and the answer relates to the difference between taking a population census and taking a sample. It depends of how the statistics of 3.1% and 4.5% were obtained. Do you have com …

I think you are trying to rediscover the G-test or likelihood ratio test. Your $H$ is a scaled version of the G statistic, which is defined as $$G=2\sum_{i=1}^m O_i \log(O_i/E_i)$$ with $E_i=n/m$. $G$ …

treatment <- relevel(treatment, ref = "B") t.test(height ~ treatment) Or alternatively treatment <- factor(treatment, levels=c("B","A")) would also have worked.

The hypothesis test functions in the stats package use classic S3 object-orientated programming. You write a function that creates a "htest" object, which is a list with a standard set of components, …

You have chosen to do a one-sided test and, obviously, order is important in a one-sided test. Your first call to fisher.test is testing the null hypothesis Pct1 = Pct2 vs the alternative that Pct1 < …

If you are using R and want use the method of Benjamini and Hochberg (1995) to control the FDR, then you can use: FDR <- p.adjust(p, method="BH") where $p$ is the vector of p-values to compute BH a …

First, ppois(20,9.8,lower.tail=FALSE) actually computes $P(X>20 | \theta=9.8)$ rather than $P(X\ge 20 | \theta=9.8)$. So to compute the p-value for $H_0$ vs $H_1$ you actually need > ppois(20-0.5, …

Nothing went wrong. The adjusted p-values are correct. Adjusted $p=1$ simply means no evidence at all for rejecting the null hypothesis. However p.adjust(data2$raw.p, method = "holm") is always b …

Unless you know the parametric form of the distribution (e.g., normal), there is no way to construct a rigorous formal test of the hypothesis using classical methods. If the sample is large, and if t …