Answer:
Without changing the sample size the change of CI may affect the results
Step-by-step explanation:
According to Central Limit theorem the sampling distribution is given by
Z= x`- u/ σ/√n
Z has in the limit a standard normal distribution and
x`= u ± zσ/√n
From the above
x`- z∝(σ/√n) ≤ u ≤ x`+ z∝(σ/√n)
This formula is used for the confidence interval with normal population and unknown standard deviation.
But if the different values of Z∝ are used the results will be different.
If the CI of 99% or 95% or 90% is used the values of acceptance and rejection regions change and therefore the results will change.
The value of Z∝ for
∝= 0.1 is ± 1.645
∝= 0.05 is ± 1.96
∝= 0.01 is ± 2.58
Suppose we get the calculated Z value equal 2.59 but we decrease the CI from 0.05 to 0.01 the acceptance region would become rejection region and the level of confidence will change.
