Which of the following is not an assumption or condition that needs to be checked for the one-proportion z-interval?
A) Success/Failure Condition
B) > 10% condition
C) Independence
D) < 10% Condition
E) Randomization
Is the answer C?
One of the assumptions is that samples are independent. Another assumption is that you are sampling less than 10% of the total population.
This should help with your answer.
No, the correct answer is D) < 10% Condition.
The < 10% condition is not an assumption or condition that needs to be checked for the one-proportion z-interval. The other options are correct assumptions or conditions that need to be met for constructing a one-proportion z-interval:
A) Success/Failure Condition: The success/failure condition requires that we have at least 10 successes and 10 failures in the sample.
B) > 10% condition: This condition requires that the sample size is no more than 10% of the population for the calculation of the standard error.
C) Independence: The independence assumption requires that the sample observations are independent of each other.
E) Randomization: Randomization refers to the random selection of the sample from the population, which is an important assumption for inference.
No, the answer is not C. The assumption or condition that is not necessary to check for the one-proportion z-interval is D) < 10% Condition.
To understand why the < 10% Condition is not required for the one-proportion z-interval, let's first understand what a one-proportion z-interval is. A one-proportion z-interval is a method to estimate a population proportion (or percentage) when you have a single sample from the population.
The assumptions and conditions that need to be checked for a one-proportion z-interval are:
A) Success/Failure Condition: This condition requires that both the number of successes and the number of failures in the sample be at least 10. It ensures that the normal distribution approximation is valid.
B) > 10% Condition: This condition states that the sample size must be less than 10% of the population size. It ensures that the sample is representative of the population.
C) Independence: This condition assumes that the sample observations are independent of each other. In other words, knowing the outcome of one observation does not influence the outcome of another observation.
E) Randomization: This condition assumes that the sample is selected randomly from the population. Random sampling helps to reduce bias and ensure that the sample is representative of the population.
The < 10% Condition is not required for a one-proportion z-interval. This condition is specifically relevant for the one-proportion z-test, where we compare a sample proportion to a hypothesized proportion. The < 10% Condition is used to check if the sample is representative of the population under the null hypothesis.
In summary, the correct answer to your question is D) < 10% Condition, as it is not an assumption or condition that needs to be checked for the one-proportion z-interval.