D3 hypothesis testing Flashcards
Which of the following best describes the null hypothesis?
A) The hypothesis that there is a significant effect.
B) The hypothesis that the results are due to chance.
C) The hypothesis that there is no significant difference.
D) The hypothesis that all sample means are equal.
C
In a hypothesis test, if the p-value is less than the significance level (α), you should:
A) Fail to reject the null hypothesis.
B) Reject the null hypothesis.
C) Increase the sample size.
D) Change the significance level.
B
A Type I error occurs when:
A) The null hypothesis is rejected when it is true.
B) The null hypothesis is not rejected when it is false.
C) The alternative hypothesis is accepted when it is false.
D) There is insufficient evidence to make a decision.
A
Which of the following tests is appropriate for comparing means of two independent samples?
A) Paired t-test
B) Independent t-test
C) Chi-square test
D) ANOVA
B
In hypothesis testing, the p-value represents:
A) The probability of obtaining a test statistic at least as extreme as the one observed, assuming the null hypothesis is true.
B) The probability that the null hypothesis is true.
C) The probability that the alternative hypothesis is true.
D) The probability of making a Type II error.
A
When performing a hypothesis test for a population proportion, which distribution is generally used?
A) Normal distribution
B) Chi-square distribution
C) t-distribution
D) Exponential distribution
A
The level of significance in hypothesis testing is typically denoted by:
A) β
B) α
C) p
D) µ
B
A p-value of 0.03 means:
A) There is a 3% chance that the null hypothesis is true.
B) There is a 3% chance that the results are due to random variation.
C) There is a 97% chance that the null hypothesis is true.
D) The probability of observing the sample results, given that the null hypothesis is true, is 3%.
D
In an ANOVA test, the null hypothesis is:
A) The variances of the populations are equal.
B) The population means are equal.
C) The sample means are different.
D) The population variances are different.
B
What does a chi-square test measure?
A) The mean difference between groups.
B) The association between categorical variables.
C) The probability of a continuous variable.
D) The difference between paired sample means.
B
The power of a test is defined as:
A) The probability of rejecting the null hypothesis.
B) The probability of accepting the null hypothesis.
C) The probability of correctly rejecting a false null hypothesis.
D) The probability of making a Type I error.
C
A Type II error occurs when:
A) The null hypothesis is rejected when it is true.
B) The null hypothesis is not rejected when it is false.
C) The alternative hypothesis is rejected when it is false.
D) There is insufficient evidence to make a decision.
A
Which of the following would increase the power of a test?
A) Increasing sample size
B) Increasing significance level
C) Reducing variance
D) All of the above
D
When should you use a paired t-test?
A) Comparing two related groups
B) Comparing two independent groups
C) Comparing frequencies
D) Comparing variances
A
The F-distribution is used in which of the following tests?
A) Chi-square test
B) t-test
C) ANOVA
D) Z-test
C