Question

1 The probability of type II error becomes bigger if the level of significance is changed from 0.01 to 0.05.

True

False

2

Increasing the sample size reduces the probability of committing a Type I and Type II simultaneously.

True

False

3

In testing a hypothesis about a population mean with an unknown
population standard deviation (*σ* ) the degrees of freedom
is used in the denominator of the test statistic.

True

False

4

When a researcher fails to reject a false null hypothesis, a Type II error has been committed.

True

False

Answer #1

1 The probability of type II error becomes bigger if the level of significance is changed from 0.01 to 0.05.

Answer : The given statement is True.

2)Increasing the sample size reduces the probability of committing a Type I and Type II simultaneously.

Answer : The given statement is True

3)In testing a hypothesis about a population mean with an
unknown population standard deviation (*σ* ) the degrees of
freedom is used in the denominator of the test statistic.

Answer : The given statement is false. In the denominator we used std error

4)When a researcher fails to reject a false null hypothesis, a Type II error has been committed.

Answer: The given statement is True

Which of the following sentences best characterizes the
relationship among Type 1 error, Type II error, Sample Size, and
the power of hypothesis testing?
hypothesis testing does not rely on sample size, Type I or Type
II error
Type I error is inflated as Type II error decreases, neither is
related to sample size
When the sample size is small, there is a higher chance of
committing a Type II error and decreased power to reject the null
hypothesis
When...

A Type II error occurs when the researcher concludes that two
sample means are not significantly different, even though in fact
the population means are different. True False
QUESTION 36 A Type II error occurs when the researcher concludes
that two sample means are significantly different, even though in
fact the population means are the same. True False

Regarding the definition of Type I and Type II error, which of
the following is correct?
A) Type I error: Fail to reject the null hypothesis when it is
actually false.
B) Type II error: Reject the null hypothesis when it is actually
true.
C) The probability of Type I error is equal to the significance
level.
D) Neither Type I error nor Type II error can be controlled by
the experimenter.

If the probability of a type II error in a hypothesis test is
0.07, what is the power of the hypothesis test? Assuming your
sample is unbiased, would increasing the size of the sample
increase or decrease the power of the hypothesis test? Please
explain your answer.

Can someone answer and explain how to do these problems?
1 Type II Error: For the roulette table in (Q6), determine which
hypothesis testing scenario has the larger Type II error
probability for a two-sided hypothesis for HO: p=18/19:
1. a) N=10,000, p=0.96 , α=0.05 OR b) N=10,000, p=0.97 ,
α=0.05.
2. a) N=10,000, p=0.96, α=0.05 OR b) N=50,000, p=0.96,
α=0.05.
3. a) N=10,000, p=0.97, α=0.05 OR b) N=10,000, p=0.97,
α=0.01.
Describe how the Type II error is influenced by...

Determine the probability of making a Type II error for the
following hypothesis test, given that μ=1061 μ=1061.
H0 : μ = 1040
H1 : μ >1040
For this test, take σ=47, n=26, and α=0.07.
P(Type II Error) =
I would really like to understand how to solve this kind of
question not just the answer if anyone has the time to explain the
logic (and formulas) it would be much appreciated

Can explain how to do these problems?
1 Type II Error: For the roulette table in (Q6), determine which
hypothesis testing scenario has the larger Type II error
probability for a two-sided hypothesis for HO: p=18/19:
1. a) N=10,000, p=0.96 , α=0.05 OR b) N=10,000, p=0.97 ,
α=0.05.
2. a) N=10,000, p=0.96, α=0.05 OR b) N=50,000, p=0.96,
α=0.05.
3. a) N=10,000, p=0.97, α=0.05 OR b) N=10,000, p=0.97, α=0.01.
Describe how the Type II error is influenced by N, p and...

T or F? If the probability of a Type II error for a given
hypothesis test is 0.40, then it must have power equal to .40
T or F? The mean of the distribution of sample means is equal to
the mean for the population from which the samples are obtained
when the samples are large, but not when they are small.

1. Setting the significance level cutoff at .10
instead of the more usual .05 increases the likelihood of
a. a Type I error.
b. a Type II error.
c. failing to reject the null hypothesis.
d. accepting the null hypothesis when, in fact, it is false.
2. A Type I error is the result of
a. improper measurement techniques on the part of the
researcher.
b. failing to reject the null hypothesis when, in fact, it is
true.
c. incorrectly...

1. Type 1 error is:
a. the probability of rejecting the alternative hypothesis even
though it is true.
b. the probability of accepting the alternative hypothesis even
though it is false.
c. the probability of rejecting the null hypothesis even though
it is true.
d. the probability of accepting the null hypothesis even though
it is false.
2.
Conclusions about population parameters made from confidence
intervals (CI) and hypothesis tests (HT) are the same when they use
the same a...

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