When conducting a significance test to determine if there is a difference between two treatments, with a quantitative response variable, treatments are given to different experimental units, we summarize the data by: Group of answer choices computing the proportion of the sample that reacted better to treatment one than treatment two. computing the mean and standard deviation of each treatment group separately. computing the difference in the responses for each experimental unit under both treatments, and then finding the mean and standard deviation of the differences. computing the difference in the proportion of the sample that reacted better to treatment one and the proportion of the sample that reacted better to treatment two.

option B computing mean and standard deviation of each sample group separately

Step-by-step explanation:

The complete question is:

When conducting a significance test to determine if there is a difference between two treatments, with a quantitative response variable, treatments are given to different experimental units, we summarize the data by:

Group of answer choices:

A) computing the proportion of the sample that reacted better to treatment one than treatment two.

B) computing the mean and standard deviation of each treatment group separately.

C) computing the difference in the responses for each experimental unit under both treatments, and then finding the mean and standard deviation of the differences.

D) computing the difference in the proportion of the sample that reacted better to treatment one and the proportion of the sample that reacted better to treatment two.

The sampling procedure must be unbiased so computing that proportion of sample only that reacted to treatment one will create biasing. The two treatment groups must be separate.

Here means and standard deviation of each treatment group must be computed separately to eliminate the effects of erroneous obseervations.

Based on above explanation, option A, C and D are nullified

the forst and 3rd

step-by-step explanation: