Answer:
Kindly check explanation
Step-by-step explanation:
The alpha level set at the beginning of of an experiment is used by the researcher to the limit the probability if making a type 1 error. The type 1 error is committed when a true null hypothesis is incorrectly rejected.
The type 2 error on the other hand is committed when fail to reject a false null hypothesis.
Hence, in other to forestall the risk of incorrectly rejecting a true null hypothesis, the alpha level is set.
When critical region is split across both tails of the distribution, The z-score boundaries at an alpha level α = .05
α = .05 (95% confidence level)
When region is split into 2:
α/2 = .05/2
α/2 = 0.025
Loooking up the z table for the Zscore with probability of 0.025
Zscore = ±1.96
The alpha level, which would be chosen at the start of an investigation, is utilized by the investigator to restrict the likelihood of making a type 1 error and the z-score will be "[tex]\pm[/tex] 1.96".
According to the question,
Whenever the genuine null hypothesis has been wrongly dismissed or discarded, a type 1 problem happens.
When the crucial zone is divided between the distribution's two tails. At an alpha level, the z-score boundaries be:
95% confidence level, α = 0.05
Now,
→ [tex]\frac{\alpha}{2} = \frac{0.05}{2}[/tex]
By applying cross-multiplication, we get
[tex]\frac{\alpha}{2} = 0.025[/tex]
hence,
By using the z table,
Z-score = [tex]\pm[/tex] 1.96
Thus the above answer is correct.
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