Social Sciences (2026) Q&A
1. Which of the following best describes the primary purpose of hypothesis testing in
statistics?
A) To describe the characteristics of a sample
B) To determine whether a sample mean differs significantly from a population mean
C) To organize data into frequency tables
D) To calculate measures of central tendency
Correct Answer: To determine whether a sample mean differs significantly from a
population mean
Rationale: Hypothesis testing is a statistical method that uses sample data to evaluate a
hypothesis about a population parameter. It allows researchers to determine whether an
observed effect is statistically significant or likely due to chance. This is a fundamental
component of inferential statistics.
2. What is the null hypothesis in a statistical test?
A) A statement that there is a relationship between variables
B) A statement that there is no effect or no relationship
C) A statement that the research hypothesis is true
D) A statement that the sample mean equals the sample standard deviation
Correct Answer: A statement that there is no effect or no relationship
Rationale: The null hypothesis (H₀) is a statement of no effect, no difference, or no
relationship. It serves as the default position that researchers attempt to reject. Rejecting the
null hypothesis provides support for the alternative hypothesis.
3. What is the alternative hypothesis in a statistical test?
,A) A statement that there is no effect or no relationship
B) A statement that the null hypothesis is true
C) A statement that there is an effect or a relationship
D) A statement that the sample is not representative
Correct Answer: A statement that there is an effect or a relationship
Rationale: The alternative hypothesis (H₁) is the statement that there is an effect, difference,
or relationship. It is the complement of the null hypothesis and represents what the researcher
is trying to support. It is also called the research hypothesis.
4. A researcher predicts that a new therapy will decrease anxiety scores. This is an example
of:
A) A non-directional hypothesis
B) A directional hypothesis
C) A null hypothesis
D) A Type II error
Correct Answer: A directional hypothesis
Rationale: A directional (one-tailed) hypothesis specifies the expected direction of the effect.
Predicting that anxiety scores will decrease is a directional hypothesis. A non-directional
hypothesis would simply state that the therapy will change anxiety scores without specifying
direction.
5. What is the significance level (alpha) in hypothesis testing?
A) The probability of correctly rejecting the null hypothesis
B) The probability of making a Type I error
C) The probability of making a Type II error
D) The probability that the null hypothesis is true
, Correct Answer: The probability of making a Type I error
Rationale: The significance level, denoted by alpha (α), is the probability of rejecting the
null hypothesis when it is actually true (Type I error). It is typically set at 0.05 or 0.01. It
defines the critical region for the test.
6. What is a Type I error?
A) Failing to reject a false null hypothesis
B) Rejecting a true null hypothesis
C) Accepting a true null hypothesis
D) Accepting a false null hypothesis
Correct Answer: Rejecting a true null hypothesis
Rationale: A Type I error occurs when a researcher rejects a null hypothesis that is actually
true. This is also known as a "false positive." The probability of making a Type I error is
equal to alpha (α).
7. What is a Type II error?
A) Rejecting a true null hypothesis
B) Failing to reject a false null hypothesis
C) Accepting a true null hypothesis
D) Rejecting a false null hypothesis
Correct Answer: Failing to reject a false null hypothesis
Rationale: A Type II error occurs when a researcher fails to reject a null hypothesis that is
actually false. This is also known as a "false negative." The probability of making a Type II
error is denoted by beta (β).
8. What is the relationship between alpha and the risk of a Type I error?