Data Handling: Inferential Statistics (College Board AP® Psychology): Flashcards
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- FrontStatistical Testing
Define statistical significance.
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Cards in this collection (12)
Define statistical significance.
Statistical significance refers to the probability that the results of a study occurred by chance rather than due to the variables being studied.
What does a researcher do with the null hypothesis when a result is statistically significant?
When a result is statistically significant, the researcher rejects the null hypothesis and concludes that the independent variable affected the dependent variable or that a genuine relationship exists.
Define Type I error.
A Type I error is a false positive, in which the researcher rejects the null hypothesis when it should have been retained.
Define Type II error.
A Type II error is a false negative, in which the researcher retains the null hypothesis when it should have been rejected.
True or False?
A result that is statistically significant and has a large effect size provides the strongest evidence that a finding is both real and meaningful.
True.
A result that combines statistical significance with a large effect size is the strongest possible evidence, because it is both unlikely to be due to chance and practically important.
Define effect size.
Effect size is a numerical measure of how meaningful or practically important a finding is, independent of whether the result is statistically significant.
What effect size value indicates a large effect?
A large effect size is 0.8 and above, indicating that the relationship or difference between conditions is strong and practically significant.
The standard threshold for statistical significance in psychological research is p ≤ ______.
The standard threshold for statistical significance in psychological research is p ≤ 0.05.
What is the trade-off between Type I and Type II errors?
Setting the significance threshold too loosely increases the risk of a Type I error, while setting it too stringently increases the risk of a Type II error.
True or False?
A statistically significant result always has practical importance.
False.
A result can be statistically significant without being practically meaningful; in very large samples, even a tiny effect size can reach statistical significance.
Define meta-analysis.
A meta-analysis synthesizes effect sizes across multiple replicated studies, producing a pooled effect size that is more reliable and generalizable than any single study could provide.
When might researchers use a more stringent significance threshold than the standard one?
Researchers use a more stringent threshold such as p ≤ 0.01 when a false positive would carry a significant human cost, like in drug trials, or when existing evidence is contradictory and greater certainty is required.
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