Respuesta :
Answer:
Random samples
Counts of successes and failures at least 15 each for each group.
Step-by-step explanation:
For making any inferences about the differences of the two independent population proportion, we assumed that the samples are of random and the counts of the success and the failures are at least 15 each for each of the groups.
From such assumption of the randomness, the observations in the population 1 will not be affected by the observations in the population 2, and also vice versa.
The assumptions of the counts refers tot he samples from each population that are big enough to justify by the normal distribution in order to model the differences between the proportions.
Therefore the assumptions that we need to make are:
-- Random samples
-- Counts of successes and failures at least 15 each for each group.
When we make inferences about the difference of two independent population proportions, we assume that it is a random sample, and the number of successes and failures are at least 15 in each group.
Two independent proportions tests involve comparing the proportions of two unrelated datasets.
For these two datasets to be regarded as an independent population, the following must be true or assumed to be true
- The datasets must represent a random sample
- Each dataset must contain at least 15 successes and failures
Hence, the above highlights are the assumptions of two independent population proportions.
Read more about independent populations at:
https://brainly.com/question/22722329
