Timeline for How to determine sample size for Chi-squared test?
Current License: CC BY-SA 4.0
7 events
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| Jan 22, 2019 at 8:43 | comment | added | LuckyPal | +1 this discussion already helped me well. The reasoning behind dat_0_better is that "dat_0 is not representing a meaningful null hypothesis at all, because in no way I would expect 20/50 spontaneous mutation of tumor" | |
| Jan 21, 2019 at 13:57 | comment | added | Sal Mangiafico | dat_0_better does represent a null hypothesis: it has a chi-square value of 0 and an effect size of 0. But it isn't the expected values for dat. It is the expected values for dat_sal in the following... There is a method to calculate expected values, chi-squared, and Cohen's w. So, for any table, there is a unique value for each of these statistics. I don't know if a variant would be chi-square distributed, but I also don't see the point in creating the variant. (dat_sal=matrix(c(0,50,20,30),nrow=2, byrow=F)); chisq.test(dat_sal)$expected; library(rcompanion); cohenW(dat_sal) | |
| Jan 21, 2019 at 11:20 | comment | added | LuckyPal | This is much closer to an answer! Can you also explain, why dat_0_better does not correctly represent the expected values? In my view, it is perfectly in line with the null hypothesis "there is no difference between the two groups". Is it that the test statistic is not chi-squared distributed if I use a different than the usual definition of expected counts? | |
| Jan 19, 2019 at 19:44 | comment | added | Sal Mangiafico | Okay, I get your question now. I've re-written my response. Hopefully it's helpful, though it's somewhat a re-statement of what you already know. | |
| Jan 19, 2019 at 19:41 | history | edited | Sal Mangiafico | CC BY-SA 4.0 | Complete re-write. |
| Jan 19, 2019 at 17:37 | comment | added | LuckyPal | Many thanks for your reply! It doesn't answer my questions though. When planning sample size, many software packages, e.g. G*power and pwr-package in R, use the effect size w. Cramér's V is obviously zero for both dat_0 and dat_0_better because they are supposed to represent the null hypothesis. I am asking whether both are correct representations of the null hypothesis because they lead to different effect sizes and hence different sample sizes. I edited the title to make my question more clear. | |
| Jan 18, 2019 at 14:10 | history | answered | Sal Mangiafico | CC BY-SA 4.0 |