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Maybe some consider it old-fashioned, but if you only want to test the null hypothesis of all groups having equal success probability, then you can define $X_k$ as number of successes in group $k$, $n_k$ as number of trials in group $k$, the estimated probability in group $k$ will be $\hat{p}_k=X_k/n_k$, and then use the variance-stabilizing transformation for the binomial, which is $$ g(p) = \arcsin \sqrt{p} $$ Such an approach was (at times) good enough for Fisher, so can be useful also today!

However, some modern authors are quite sceptical of the arcsine transformation, see for example http://www.mun.ca/biology/dschneider/b7932/B7932Final10Dec2010.pdf But this authors are concerned with problems such as prediction, where they show the arcsine can lead to problems. If you are only concerned with hypothesis testing, it should be OK. A more modern approach could use logistic regression.

Maybe some consider it old-fashioned, but if you only want to test the null hypothesis of all groups having equal success probability, then you can define $X_k$ as number of successes in group $k$, $n_k$ as number of trials in group $k$, the estimated probability in group $k$ will be $\hat{p}_k=X_k/n_k$, and then use the variance-stabilizing transformation for the binomial, which is $$ g(p) = \arcsin \sqrt{p} $$ Such an approach was (at times) good enough for Fisher, so can be useful also today!

However, some modern authors are quite sceptical of the arcsine transformation, see for example The arcsine is asinine: the analysis of proportions in ecology by David I. Warton and Francis K. C. Hui.

But this authors are concerned with problems such as prediction, where they show the arcsine can lead to problems. If you are only concerned with hypothesis testing, it should be OK. A more modern approach could use logistic regression.

Maybe some consider it old-fashioned, but if you only want to test the null hypothesis of all groups having equal success probability, then you can define $X_k$ as number of successes in group $k$, $n_k$ as number of trials in group $k$, the estimated probability in group $k$ will be $\hat{p}_k=X_k/n_k$, and then use the variance-stabilizing transformation for the binomial, which is $$ g(p) = \arcsin \sqrt(p) $$ Such an approach was (at times) good enough for Fisher, so can be useful also today!

Maybe some consider it old-fashioned, but if you only want to test the null hypothesis of all groups having equal success probability, then you can define $X_k$ as number of successes in group $k$, $n_k$ as number of trials in group $k$, the estimated probability in group $k$ will be $\hat{p}_k=X_k/n_k$, and then use the variance-stabilizing transformation for the binomial, which is $$ g(p) = \arcsin \sqrt{p} $$ Such an approach was (at times) good enough for Fisher, so can be useful also today!

However, some modern authors are quite sceptical of the arcsine transformation, see for example http://www.mun.ca/biology/dschneider/b7932/B7932Final10Dec2010.pdf But this authors are concerned with problems such as prediction, where they show the arcsine can lead to problems. If you are only concerned with hypothesis testing, it should be OK. A more modern approach could use logistic regression.

Maybe some consider it old-fashioned, b ut if you only want to test the null hypothesis of all groups have equal success probabilty, then you can define $X_k$ as number of successes in group $k$, $n_k$ as number of trials in ghroup $k$, the estimated probability in grouo $k$ will be $\hat{p}_k=X_k/n_k$, and then use the variance-stabilizing transformation for the binomial, which is $$ g(p) = \arcsin \sqrt(p) $$ Suh approach was (at times) good enough for Fisher, so can be useful also today!

Maybe some consider it old-fashioned, but if you only want to test the null hypothesis of all groups having equal success probability, then you can define $X_k$ as number of successes in group $k$, $n_k$ as number of trials in group $k$, the estimated probability in group $k$ will be $\hat{p}_k=X_k/n_k$, and then use the variance-stabilizing transformation for the binomial, which is $$ g(p) = \arcsin \sqrt(p) $$ Such an approach was (at times) good enough for Fisher, so can be useful also today!