Evaluating statistical claims, observational studies, and experiments: practice and worksheets
Evaluate statistical claims by checking study design, sampling, random assignment, bias, and confounding variables.
Concept overview
The design of a study determines which conclusions are justified. Random sampling supports population generalization, while random assignment in an experiment supports cause-and-effect conclusions.
Key points
- Distinguish an observational study from an experiment with imposed treatments.
- Look for selection bias, nonresponse, leading questions, and confounding variables.
- Do not turn correlation from an observational study into a causal claim.
Worked example
If volunteers choose whether to use a study app, a higher score among users does not prove the app caused the difference because motivation may be a confounding variable.
Try it free
Answer a few questions without signing in. You will get instant feedback, but open-preview progress is not saved.
Questions by difficulty
Frequently asked questions
How should I practice Evaluating statistical claims, observational studies, and experiments?
Start with foundation questions, check each answer, and move up a level after several correct answers in a row.
How many practice questions are available?
IQClub currently has 2 published questions for this skill.
Can I use this for test preparation?
Yes. The practice reinforces school mathematics and the same reasoning used in high-school tests and entrance exams.