Math · Problem-Solving and Data Analysis · Q.G.
Evaluating statistical claims: Observational studies and experiments
Observational study vs experiment is the entire skill. Random sample lets you generalize to the population. Random assignment lets you talk about cause.
What Bluebook is testing
Q.G. is evaluating statistical claims: what a study design can and cannot support. Treatments, confounding, and sampling.
How it shows up
A short research blurb, then 'which conclusion is appropriate.' No calculation. Four overconfident sentences.
A method that survives Module 2
Underline the design: who was sampled, who was assigned, what was measured. If there is no random assignment, drop every 'causes' / 'leads to' choice. If the sample is 'students at one high school,' the population is not 'all teenagers.'
Traps that look like knowledge
- Causal language from an observational study
- Generalizing past the sampled population
- Confusing random sample (who is in the study) with random assignment (who gets the treatment)
- Declaring a result 'proven' from one study
Questions
- What is "Evaluating statistical claims: Observational studies and experiments" on the Digital SAT?
- It is an official College Board skill in Digital SAT Math. The wording on Bluebook matches the domain taxonomy Drilled uses in drills, analytics, and the notebook.
- How does Drilled train Evaluating statistical claims: Observational studies and experiments?
- Filter the bank to the skill, run a timed drill, autopsy every miss, and let the notebook bring the same pattern back. Domain drills and full section sims include the skill at the official mix.
- What is the most common trap on Evaluating statistical claims: Observational studies and experiments?
- Causal language from an observational study
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