What does the alternative hypothesis typically represent?
The statement that is always accepted in hypothesis testing.
The statement that is always rejected in hypothesis testing.
The statement of no effect or no difference.
The opposite of the null hypothesis.
What does a small standard deviation indicate about a dataset?
The data points are spread out widely from the mean
The data is skewed to the left
The data is skewed to the right
The data points are clustered closely around the mean
Which of the following best describes a Type II error?
Rejecting the null hypothesis when it is actually true.
Rejecting the alternative hypothesis when it is actually true.
Accepting the alternative hypothesis when it is actually false.
Failing to reject the null hypothesis when it is actually false.
Which of the following correctly defines the null hypothesis?
The statement we are trying to prove.
The statement that is rejected if the p-value is greater than the significance level.
The statement that is accepted if the p-value is less than the significance level.
What does a variance of zero indicate about a dataset?
The mean is zero.
The data is perfectly normally distributed.
All data points are the same.
The dataset has a large amount of variation.
A researcher wants to survey a sample of students from a university. They randomly select 5 departments and then survey every student within those departments. What sampling method is being used?
Stratified Sampling
Cluster Sampling
Systematic Sampling
Simple Random Sampling
What is the commonly used significance level (alpha) in hypothesis testing?
0.50
0.05
0.01
0.10
A frequency distribution shows the:
Number of times each value of a variable occurs in a dataset
Spread or variability of data points around the mean
Average value of a variable in a dataset
Probability of a particular outcome in an experiment
What does the significance level (alpha) represent in hypothesis testing?
The strength of the relationship between variables.
The probability of obtaining the observed data.
The probability of making a Type II error.
The threshold below which we reject the null hypothesis.
Which type of error occurs when we reject a true null hypothesis?
Sampling error
Type II error
Type III error
Type I error