A type II error (in hypotheses testing), is the error of?

Not rejecting a false null hypothesis

A result is called “statistically significant” whenever

The p-value is less or equal to the significance level

In hypothesis testing, a Type II error occurs when

The null hypothesis is not rejected when the null hypothesis is false

If the degrees of freedom of the t-distribution increase to infinity the distribution is approximatelly equal to?

The standard normal distribution

One problem with hypothesis testing is that a real effect may not be detected. This is most likely to occur when:

the effect is small and the sample size is small.

Whether the hypothesis test is one-sided or two-sided, determines if

If the rejection region is located in one or two tails of the distribution

The definition (in words) of the p-value is:

The minimum significance level at which H0 will be rejected.

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Combined: Stat 1: Tutorial 6B + Stat 1: Seminar 7B

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Answers

1
A type II error (in hypotheses testing), is the error of?
Rejecting a true alternative hypothesis
Rejecting a true null hypothesis
Not rejecting a false null hypothesis
Not rejecting a fals alternative hypothesis
2
A result is called “statistically significant” whenever
The null hypothesis is true.
The alternative hypothesis is true
The p-value is less or equal to the significance level
The p-value is larger than the significance level.
3
In hypothesis testing, a Type II error occurs when
The null hypothesis is rejected when the research hypothesis is true.
The null hypothesis is rejected when the null hypothesis is true.
The null hypothesis is not rejected when the null hypothesis is false
The null hypothesis is not rejected when the null hypothesisis true
4
The null and research hypothesis consider statements regarding:
it depends, population paramaters or sample statistics
population paramaters
sample paramaters
sample statistics
5
If the degrees of freedom of the t-distribution increase to infinity the distribution is approximatelly equal to?
The χ2 distribution
The binomial distribution.
The uniform distribution
The standard normal distribution
6
1,000 randomly selected students were asked if they ever visited Artis. 54% of the students said they had. The 54% is?
it is a random number
it is a margin of error
It is a population proportion
It is a sample proportion
7
One problem with hypothesis testing is that a real effect may not be detected. This is most likely to occur when:
the effect is large and the sample size is small.
the effect is small and the sample size is small.
the effect is small and the sample size is large.
the effect is large and the sample size is large.
8
Whether the hypothesis test is one-sided or two-sided, determines if
If the rejection region is located in one or two tails of the distribution
none of the above
If the two extreme values (min or max) of the sample need to be rejected
If the hypothesis has one or possible two conclusions
9
The definition (in words) of the p-value is:
The probability that the test statistic will fall in the rejection region
The probability that the sample will be drawn randomly.
The probability of making a type II error.
The minimum significance level at which H0 will be rejected.
10
In a proper research study you should determine the rejection region:
Can be at any point in time
After you take a sample
Before you take a sample
During the sampling process
11
When constructing a confidence interval for μ when σ is unknown, we should not use:
The sample mean
The z-distribution
The t-distribution.
A minus sign
12
Increasing the power of a given hypothesis test (keeping α constant) can be realized by:
Doing nothing
Increasing n
Converting the rejection region.
Removing H0

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