We wish to analyze these data with the two independent-samples model, where our goal is to make inference for whether or not the distribution of the expression of this gene is associated with tumor status, which has levels {tumor, healthy}. To do this, what must we assume about these measurements? If these assumptions are true, then what is the probability distribution of the random variable for which the first healthy tissue's gene expression measurement of 202.90000 is assumed to be a realization? Specify as much information about this distribution as possible.
A) We must assume that the measurements are independent and identically distributed (iid) for both tumor and healthy tissues. If these assumptions are true, then the probability distribution of the random variable for which the first healthy tissue's gene expression measurement of 202.90000 is assumed to be a realization follows a normal distribution, given the large sample size and central limit theorem.
B) We must assume that the measurements are correlated between tumor and healthy tissues. If these assumptions are true, then the probability distribution of the random variable for which the first healthy tissue's gene expression measurement of 202.90000 is assumed to be a realization follows a t-distribution, given the small sample size and non-normality of the data.
C) We must assume that the measurements have equal variance for both tumor and healthy tissues. If these assumptions are true, then the probability distribution of the random variable for which the first healthy tissue's gene expression measurement of 202.90000 is assumed to be a realization follows a chi-square distribution, given the homogeneity of variances.
D) We must assume that the measurements have unequal variance for tumor and healthy tissues. If these assumptions are true, then the probability distribution of the random variable for which the first healthy tissue's gene expression measurement of 202.90000 is assumed to be a realization follows a F-distribution, given the heterogeneity of variances.
