Hypothesis test and finding type 2 error probability

In summary, the duration of treating a disease by a standard therapy with a mean of 15 days is being compared to a new therapy that claims to reduce treatment time. To test this claim, 70 patients will be treated with the new therapy and their recovery times will be recorded. The hypotheses are formulated as H0: μ = μ0 = 15 and H1: μ < μ0 = 15, with a rejection region of Z < -1.645 at a significance level of α = 0.05. Using a sample mean of x = 14.6 and standard deviation of s = 3.0, the calculated Z score is -1.12, which does not fall in the rejection region, leading
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toothpaste666
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Homework Statement

From extensive records it is known that the duration of treating a disease by a standard therapy has a mean of 15 days. It is claimed that a new therapy can reduce the treatment time. To test this claim, the new therapy is to be tried on 70 patients and their times to recovery are to be recorded.
(a) Formulate the hypotheses and determine the rejection region of the test with a level of significance α = 0.05.
(b) If x = 14.6 and s = 3.0, what does the test conclude?
(c) Repeat (a) and (b) using the p-value.
(d) Using σ = 3.0, what is the Type II error probability β(μ′ ) of the test for the alternative μ′ =14?

The Attempt at a Solution


a) H0: μ= μ0 = 15
H1: μ < μ0 = 15
since the alternative hypotheses is μ1 < μ0 we reject H0 if Z < -zα = -z.05 = -1.645

b) X = 14.6, s = 3

Z = (X - μ)/(s/sqrt(n)) = (14.6 - 15)/(3/sqrt(70)) = -1.12 > -1.645 so we cannot reject the null hypothesis .

c) the P value is P(Z < -1.12) =.1314
I am not sure how they want me to use it in a) and b) though

d) μ1 = 14 μ0 = 15
μ1< μ0 and it is a one sided test
so we use
Z < -zα + sqrt(n)((μ0 - μ1)/σ) = -1.96 + sqrt(70)((15-14)/3) = .829
Z < .829
γ = P(Z < .829) = .7967
β = 1 - γ = 1 - .7967 = .2033

I am not sure if I am understanding part d) correctly either
 
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  • #2
For part d), you used the 2-sided Z score for alpha = .05. It should be the same as the one used in parts a and b, right? The calculation steps look correct to me.

Also, for using the p-value, you simply would say to reject the null hypothesis if p < alpha.
You could write, H0: p( mu ≥ mu_0 ) ≥ .05, Ha: p(mu < mu_0) >.95 ...though I have never seen a null hypothesis written in terms of the probability.
 
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oops yes you are right. thank you
 

1. What is a hypothesis test?

A hypothesis test is a statistical method used to determine whether a specific assumption or hypothesis about a population is supported by the data. It involves setting up null and alternative hypotheses, collecting data, and using statistical analysis to determine the probability of obtaining the observed results if the null hypothesis is true.

2. What is a type 2 error in a hypothesis test?

A type 2 error, also known as a false negative, occurs when a hypothesis test incorrectly fails to reject the null hypothesis when it is actually false. This means that the test has failed to detect a significant difference or relationship that actually exists in the population.

3. How is the probability of a type 2 error calculated?

The probability of a type 2 error, denoted as beta (β), is calculated by finding the area under the sampling distribution curve that falls within the critical region, or the region where the null hypothesis would be rejected. This is typically done using statistical software or by looking up values in a table based on the sample size and chosen significance level.

4. How does the significance level affect the probability of a type 2 error?

The significance level, denoted as alpha (α), is the predetermined threshold for rejecting the null hypothesis. It is typically set at 0.05 or 0.01. A lower significance level means that the probability of a type 1 error (rejecting the null hypothesis when it is actually true) is reduced, but the probability of a type 2 error increases. Conversely, a higher significance level means a lower probability of a type 2 error, but a higher probability of a type 1 error.

5. How can the probability of a type 2 error be minimized?

The probability of a type 2 error can be reduced by increasing the sample size, which leads to a narrower sampling distribution and a smaller critical region. Additionally, choosing a higher significance level or using a more powerful statistical test can also decrease the probability of a type 2 error. It is important to balance these factors with the potential consequences of a type 1 error and the resources available for the study.

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