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.
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Questions by difficulty
Evaluating statistical claims, observational studies, and experiments worksheet practice
Use the printable worksheet for independent practice, then check the answer key and worked explanations.
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 30 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.