Question

Setting the significance level cutoff at .10 instead of the more usual .05 increases the likelihood of A. a Type I error. B. accepting the null hypothesis when, in fact, it is false. C. a Type II error. D. failing to reject the null hypothesis.

Answer #1

As we are increasing the level of significance here from 0.05 to
0.1, therefore we would be more easily able to reject the null
hypothesis here. For null hypothesis to be rejected, we want
p-value of the test to be less than the level of signficance,
therefore lesser the value of level of significance, easier it is
for the null hypothesis to get rejected. As it is easier for null
hypothesis to be rejected, **therefore B,D are incorrect
here.**

Type I error is the probability of rejecting a true null
hypothesis, as the rejection of null hypothesis has become more
likely with a greater level of significance, **therefore A is
the correct answer here.**

Type II error is not rejecting but retaining the null hypothesis
here. **therefore C is incorrect here.**

**Therefore A is the correct answer here.**

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...

If a directional, .05 level of significance (predicted ‘lower
than’) had been chosen, what z-score would be needed for the
difference between X and µ to be significant?
A. -1.65
B. -1.96
C. -2.33
D. +/- 1.65
If the probability of finding a difference that really does
exist is .65 (correctly rejecting the null hypothesis when the null
hypothesis really is false), what is the probability of the Type II
error?
A. .05 B. .95 C. .35 D. .65 also

1. Insofar as we must generalize from a sample to a population,
the observed difference between the sample mean and the
hypothesized population mean a) can't be interpreted at face value.
b) might be due to variability or chance. c) might be real. d) is
described by all of the above
2. The advantage of a one-tailed test is that it increases the
likelihood of detecting a a) false null hypothesis. b) false null
hypothesis in the direction of concern....

You complete a hypothesis test using a = .05, and based
on the evidence from the sample, your decision is to reject the
null hypothesis. If the treatment actually has no effect, which of
the following is true?
Group of answer choices
You have made a Type I error.
You have made a Type II error.
You might have made a Type I error, but the probability is only
5% at most.
You have made the correct decision.
For a...

An alpha (significance) level is set at .05 for a research
study, What does this mean?
A. A researcher is willing to reject the null hypothesis if the
probability of the null hypothesis being true is lower than
.05.
B. A researcher is willing to reject the null hypothesis if the
probability of the alternative hypothesis being true is lower than
.05.
C. A researcher is willing to reject the null hypothesis if the
probability of the null hypothesis being...

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.

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...

ohn ran an experiment and found the "t" value to be significant
at the .05 level. In actual fact the null hypothesis is true. What
error has John committed and what are his chances of doing so?
Type I error; 1 chance in 20.
Type I error; 1 chance in 100.
Type II error; 1 chance in 20.
Type II error; about 85% chance

Question 1 : The answers listed below are characteristics of a
Type I error EXCEPT for one. Select the characteristic that is not
for Type I error
a) upsetting status quo for falsehood
b) a 'missed opportunity'
c) reject null hypothesis with null is true
question 2: The answers listed below are characteristics of a
Type II error EXCEPT for one. Select the characteristic that is not
for Type II error
a) do not reject null hypothesis when it is...

Would increasing the significance level of a hypothesis test
increase or decrease the likelihood of a researcher making a Type I
error?

ADVERTISEMENT

Get Answers For Free

Most questions answered within 1 hours.

ADVERTISEMENT

asked 1 hour ago

asked 1 hour ago

asked 1 hour ago

asked 1 hour ago

asked 2 hours ago

asked 2 hours ago

asked 2 hours ago

asked 2 hours ago

asked 3 hours ago

asked 3 hours ago

asked 3 hours ago

asked 3 hours ago