Do sample sizes have to be the same for t-test?
If sample sizes in both conditions are equal, the t-test is very robust against unequal variances. If sample sizes are unequal, unequal variances can influence the Type 1 error rate of the t-test by either increasing or decreasing the Type 1 error rate from the nominal (often 0.05) alpha level.
What is the degree of freedom for two sample t-test?
The degrees of freedom parameter for looking up the t‐value is the smaller of n 1 – 1 and n 2– 1. The degrees of freedom is the smaller of (6 – 1) and (9 – 1), or 5. A 90 percent confidence interval is equivalent to an alpha level of 0.10, which is then halved to give 0.05.
Can A Wilcoxon rank sum test be performed with an unequal sample size?
It is my understanding that it is okay to perform the Wilcoxon Rank Sum test with slightly unequal sample sizes. I just don’t understand how the Wilcoxon accounts for the missing numbers in one group given that it subtracts a random score of one group to a random score of another group.
What do you need to know about the Wilcoxon test?
For the Wilcoxon test, a p-value is the probability of getting a test statistic as large or larger assuming both distributions are the same. In addition to a p-value we would like some estimated measure of how these distributions differ. The wilcox.test function provides this information when we set conf.int = TRUE.
Can a rank sum test be used with a different sample size?
If it did what you say in the question, that could work perfectly well with even very different sample sizes (since you could sample either with replacement so having the same sample size would be unnecessary), but that’s not how it works. As the name suggests, the rank-sum test sums the ranks in one of the samples.
Is the null hypothesis equal to the Wilcoxon test?
Whereas the null hypothesis of the two-sample t test is equal means, the null hypothesis of the Wilcoxon test is usually taken as equal medians.