RESEARCH METHODS EXAM 3 EXAM
SCRIPT 2026 COMPREHENSIVE
QUESTIONS AND VERIFIED SOLUTIONS
◉ Statistical Significance
Answer: A finding is statistically significant when the probability of
its occurrence under the null hypothesis is below the alpha level
(typically .05)
Larger sample sizes and effect sizes make it easier to reach
significance
Significance does not mean the effect is large or important , only that
it's unlikely due to chance
Researchers should report both p values and effect sizes to give a
fuller picture
◉ Type I Error (α)
Answer: Rejecting H₀ when it is actually true (false positive)
Probability equals the alpha level (often 5%)
,Example: concluding therapy works when it actually doesn't
◉ Type II Error (β)
Answer: Failing to reject H₀ when it is false (false negative)
Example: missing a true treatment effect because of a small sample
Power (1 − β): probability of correctly rejecting a false H₀
Larger samples increase power
◉ Decision matrix for type I and type II errors
Answer:
◉ Choosing a Significance Level
Answer: Lower α → fewer Type I errors but more Type II errors.
Exploratory research → higher α acceptable (.10 or even .25).
Confirmatory/published research → lower α (.05 or .01) preferred
, ◉ Possible reasons for nonsignificance
Answer: Weak manipulation of the IV
Unreliable or insensitive measures.
Small sample size (low power)
Truly small or absent effect
◉ Power analysis
Answer: a statistical method to determine the acceptable sample
size that will best detect the true effect of the independent variable
◉ Factors affecting power
Answer: Alpha level (smaller α → lower power).
Effect size (larger → higher power).
Sample size (larger → higher power).
◉ The Importance of Replications
Answer: Single studies are never final proof.
Replication helps confirm that findings aren't due to chance or
unique conditions.
SCRIPT 2026 COMPREHENSIVE
QUESTIONS AND VERIFIED SOLUTIONS
◉ Statistical Significance
Answer: A finding is statistically significant when the probability of
its occurrence under the null hypothesis is below the alpha level
(typically .05)
Larger sample sizes and effect sizes make it easier to reach
significance
Significance does not mean the effect is large or important , only that
it's unlikely due to chance
Researchers should report both p values and effect sizes to give a
fuller picture
◉ Type I Error (α)
Answer: Rejecting H₀ when it is actually true (false positive)
Probability equals the alpha level (often 5%)
,Example: concluding therapy works when it actually doesn't
◉ Type II Error (β)
Answer: Failing to reject H₀ when it is false (false negative)
Example: missing a true treatment effect because of a small sample
Power (1 − β): probability of correctly rejecting a false H₀
Larger samples increase power
◉ Decision matrix for type I and type II errors
Answer:
◉ Choosing a Significance Level
Answer: Lower α → fewer Type I errors but more Type II errors.
Exploratory research → higher α acceptable (.10 or even .25).
Confirmatory/published research → lower α (.05 or .01) preferred
, ◉ Possible reasons for nonsignificance
Answer: Weak manipulation of the IV
Unreliable or insensitive measures.
Small sample size (low power)
Truly small or absent effect
◉ Power analysis
Answer: a statistical method to determine the acceptable sample
size that will best detect the true effect of the independent variable
◉ Factors affecting power
Answer: Alpha level (smaller α → lower power).
Effect size (larger → higher power).
Sample size (larger → higher power).
◉ The Importance of Replications
Answer: Single studies are never final proof.
Replication helps confirm that findings aren't due to chance or
unique conditions.
