AP Statistics Flashcards: Inference and Experiments
Written by AP Content Team, Verified for 2026 AP Exams, Last updated: May 2026
Review key ideas with interactive flashcards. This set includes 11 cards to help you master important concepts.
How does statistical significance relate to causation?
Statistically significant differences between treatment groups serve as the evidence needed to claim that the treatments caused the observed effect.
Card 1 of 11
All Flashcards (11)
How does statistical significance relate to causation?
Statistically significant differences between treatment groups serve as the evidence needed to claim that the treatments caused the observed effect.
A study on a new drug uses volunteers from a single hospital. If the results are statistically significant, can they be generalized to the entire country's population?
No, the results cannot be generalized because the experimental units (volunteers from one hospital) are not representative of the larger group (the entire country).
If a difference between two randomly assigned treatment groups is NOT statistically significant, what is a likely explanation for the observed difference?
If the difference is not statistically significant, it is likely that the observed difference could be due to chance variation alone.
Under what condition can the results of an experiment be generalized to a larger population?
Results can be generalized to a larger group if the experimental units used in the study are representative of that group.
Why is it important to interpret the results of a 'well-designed' experiment specifically?
A well-designed experiment, featuring elements like random assignment, is necessary to make valid conclusions about causation and statistical significance.
What two conditions are necessary to claim an experiment's results show causation and are generalizable?
To show causation, random assignment and statistically significant results are needed; to be generalizable, the experimental units must be representative of the larger group.
What is the primary benefit of using random assignment in an experiment?
Random assignment allows researchers to conclude that observed changes are unlikely to be due to chance, making it possible to establish a cause-and-effect relationship.
In a well-designed experiment, a new fertilizer shows a statistically significant increase in crop yield compared to the old fertilizer. What can be concluded?
Because the difference is statistically significant and the experiment was well-designed (implying random assignment), we can conclude the new fertilizer caused the increase in crop yield.
What is required to conclude that a treatment caused an observed effect in an experiment?
Statistically significant differences between randomly assigned treatment groups are required as evidence that the treatments caused the effect.
What is statistical inference?
Statistical inference is the process of attributing conclusions based on data to the broader distribution from which the data was collected.
What does it mean for results to be 'statistically significant'?
A result is statistically significant if the observed changes are unlikely to have occurred due to random chance alone.