Hypothesis test: Find the critical value of Z, Zc

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In summary: Substituting 95% as p into invNorm will give you the Z value you need.In summary, the problem involves testing whether the mean price in London is different from the national mean of £1.20 with a standard deviation of 5p. The null hypothesis is that the means are equal, and the alternative hypothesis is that they are not equal. The test statistic is -1.518, and the critical Z-value for a two-tailed test can be obtained using the invNorm function on a calculator or by using the standard normal table. The critical Z-value will be the number of standard deviations that captures 95% of the area under the curve, with half of that area above and half below the middle range.
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so_gr_lo
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Homework Statement
Question:
The national price of a good is distributed normally with mean £1.20, standard deviation 5p. Sample data is for price in London, determine whether there is sufficient evidence at 5% level to suggest that the mean price in London is different to the national mean. Standard deviation same for both.
Relevant Equations
Test statistic = sample mean X - μ /sqrt(σ^2 / n) n = sample size
Question:
The national price of a good is distributed normally with mean £1.20, standard deviation 5p. Sample data is for price in London, determine whether there is sufficient evidence at 5% level to suggest that the mean price in London is different to the national mean. Standard deviation same for both.

attempt at solution:

H0: μ = 1.20
H1:μ≠ 1.20 (two -tailed test)

standard deviation: 0.05
national price mean: 1.20
sample data mean: μ = 1.176
Test statistic: Z = -1.518

How can I find the critical value of Z, Zc, on a calculator (CASIO fx-991EX classwiz). And how would I compare it to the test statistic to Zc in order to test the hypothesis. Do I test if -1.528 > Zc or -1.528 < Zc, not sure how to do it for two-tailed test.

thanks
 
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Minor point of notation. You should use [itex]\overline{X}[/itex] ("Xbar") for the sample mean to distinguish it from the population (distribution) mean.

The critical Z-value for a two tailed test will be the number of standard deviations Zc such that the probability that a standard normal RV is within that range is 1 - significance i.e the probability it is beyond that range is 5%.
P(-Zc < Z < Zc) = 95% i.e. P( Z<-Zc or Zc < Z) = 5%.

I am not familiar with your model but if it has an invNorm function that will give you invNorm(p)= z such
that P(Z < z) = p. To use this to get the two tailed range remember that the probability of being within is, in your case 95% and being beyond is 5%. Due to symmetry half that 5% is above and half is below the middle range. Thus P(Z < Zc) =(1 - 5%/2) (1 - upper tail probability).

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What is a critical value of Z?

The critical value of Z, also known as Zc, is a statistical value that is used in hypothesis testing to determine the likelihood of obtaining a certain result by chance. It is based on the level of significance chosen for the test and is used to compare the test statistic to determine if the null hypothesis should be rejected or not.

How is the critical value of Z calculated?

The critical value of Z is calculated using a statistical table or a calculator. The calculation depends on the level of significance chosen and the type of test being conducted (one-tailed or two-tailed). It is usually denoted by the Greek letter "alpha" (α) and is typically set at 0.05 or 0.01 for most hypothesis tests.

Why is it important to find the critical value of Z?

The critical value of Z is important because it helps determine the statistical significance of the results obtained from a hypothesis test. It allows researchers to make informed decisions about whether to reject or fail to reject the null hypothesis, which is a crucial step in drawing conclusions from a study or experiment.

What does the critical value of Z represent?

The critical value of Z represents the cutoff point for the test statistic. If the test statistic falls beyond this critical value, it is considered statistically significant and the null hypothesis is rejected. On the other hand, if the test statistic falls within the critical value, it is not considered statistically significant and the null hypothesis cannot be rejected.

How does the level of significance affect the critical value of Z?

The level of significance, denoted by the Greek letter "alpha" (α), is the probability of making a Type I error (rejecting the null hypothesis when it is actually true). As the level of significance decreases, the critical value of Z increases. This means that the test becomes more stringent and requires stronger evidence to reject the null hypothesis.

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