Should you observe, survey or experiment?
You will be able to: Match an investigative question to a study design and identify treatments and outcomes.
Should you observe, survey or experiment?
A teacher wants to know whether spaced practice improves retention. Watching students choose their own study method differs from assigning study methods.
A useful starting point: How unusual is a value in its own group? →
Words and symbols before equations
- Observational study
- Records variables without imposing treatments.
- Experiment
- Assigns conditions to experimental units.
- Explanatory variable
- A possible predictor or assigned factor.
- Response variable
- The measured outcome.
- Census
- Collection from every member of a defined population.
What this picture assumes
This design map assumes competent implementation, suitable ethical safeguards and valid measurement. Random assignment supports causal analysis; a real effect still requires evidence beyond chance variation.
Read the picture in three steps
- Read the axes and labels first. Identify what each symbol and line represents. Read the units and fixed conditions before comparing quantities.
- Population scope: No representative selection: limit generalization. Causal scope: No random assignment: confounding remains. Still required: Valid measurements, ethics and chance-aware analysis.
- Check what the picture assumes below. Use the Explore task to predict one change before moving a control.
Connect the picture to the mathematics
A survey asks a standard set of questions and is observational. A prospective observational study follows units into the future; a retrospective study uses their past records. Neither imposes treatments.
An experiment imposes conditions. The experimental unit is what receives the treatment: if an entire classroom is assigned one method, the classroom—not each worksheet—is the unit. With two factors, treatments are combinations of their levels.
Phrase the question to specify variables, the desired analysis and applicable conclusions. “Estimate the mean retention score” seeks a range of plausible population means; “Does spaced practice increase the mean score?” states a directional comparison. Formal intervals and tests come later. Protect consent and private information in either design.
A worked example, step by step
Forty volunteers are randomly assigned to spaced or one-session practice; both groups later take the same quiz. Identify the design elements.
- This is an experiment because the researcher imposes study methods.
- One volunteer is the experimental unit; study method is the explanatory factor with two treatment levels.
- Quiz score after one week is the response.
- Random assignment supports causal analysis if differences exceed chance variation; volunteer recruitment limits generalization.
Randomly choosing people is not the same as randomly assigning conditions to those people.
Is following students for a year necessarily an experiment?
Compare with an explanation
No. Following into the future is prospective; it is an experiment only if conditions are imposed.
Predict. Change one thing. Explain.
Toggle random selection and random assignment independently. Match each combination to the conclusions the design can support.
On narrow screens, swipe or scroll diagrams sideways to read all labels.
Population scope: No representative selection: limit generalization. Causal scope: No random assignment: confounding remains. Still required: Valid measurements, ethics and chance-aware analysis.
| Question | Conclusion |
|---|---|
| Population scope | No representative selection: limit generalization. |
| Causal scope | No random assignment: confounding remains. |
| Still required | Valid measurements, ethics and chance-aware analysis. |
This design map assumes competent implementation, suitable ethical safeguards and valid measurement. Random assignment supports causal analysis; a real effect still requires evidence beyond chance variation.
Explain what you noticed: Answer the investigation prompt above. State one observation and explain it using the data values, graph scales, summary statistics or study-design conditions. Identify what the representation cannot tell you.
Apply the idea to a fresh problem Practice →Show what you understand.
Two original questions are a starting check, not proof of mastery. Explain your choice before revealing the answer.
Original written challenge
4 points · self-check · not an official AP questionA researcher records students’ chosen sleep schedules and later exam scores. Classify the study, identify variables and suggest a confounder.
This response is not submitted or saved. Copy it before leaving.
Compare with the answer and four-point rubric
- 1 point: It is prospective observational if students are followed forward.
- 1 point: Chosen sleep schedule is the explanatory variable; score is the response.
- 1 point: Workload could influence both sleep schedule and scores.
- 1 point: The study can show an association; without random assignment, it cannot isolate a causal sleep effect.
Accept equivalent correct methods and explanations. This is a Refresh Kid teaching rubric, not an official AP scoring guideline.
Retrieve it before you reveal it.
RECALL 1What separates experiments from observational studies?
Whether treatments are imposed.
RECALL 2What is a treatment in a multifactor experiment?
A combination of factor levels.
RECALL 3Does a census eliminate all errors?
No; measurement and nonresponse problems remain possible.
Revisit these tomorrow and a week later. Try a fresh problem and explain why the method applies.
Should you observe, survey or experiment?
- Survey: ask; observational study: observe; experiment: impose.
- Identify unit, factor, treatments and response.
- A census attempts to measure the whole population but may still suffer measurement or nonresponse errors.
Remember: Randomly choosing people is not the same as randomly assigning conditions to those people.
Conditions: This design map assumes competent implementation, suitable ethical safeguards and valid measurement. Random assignment supports causal analysis; a real effect still requires evidence beyond chance variation.
Refresh Kid · AP Statistics Unit 1 · Objectives 1.10.A, 1.10.B, 1.10.C, 1.10.D · Review edition
Framework, scope and review status
Mapped to College Board, AP Statistics CED, Topic 1.10, objectives 1.10.A, 1.10.B, 1.10.C, 1.10.D. Framework effective Fall 2026, checked September 17, 2026. Unit 1 includes one-variable data and data collection; it is part of the revised five-unit course.
Examples and datasets are synthetic, independently authored teaching material. Quartile calculations state a median-of-halves convention. Outlier screens identify values to investigate, not data to discard. Random selection and random assignment have different inferential roles. These lessons introduce design and descriptive reasoning; formal inference comes in later units.
The Organic Chemistry Tutor companion title and destination were checked; the full video was not reviewed. Khan Academy’s destination was checked, but its lesson content was not fully readable by the research tool. OpenStax provides optional reference reading. No provider scripts, questions or graphics were copied. Refresh Kid is not affiliated with these providers.
GitHub’s 3D website collection informed optional spatial inspection. Our original sampling model uses self-hosted Three.js with its MIT license. It shows labeled units in four groups; camera rotation does not change the sampling procedure. Quantitative graphs remain 2D to avoid perspective distortion. Complete labeled diagrams, selected IDs and explanations remain available without 3D.
Independent teacher review and observation of students remain pending. Technical checks do not certify statistical accuracy, accessibility or learning effectiveness. This is a review edition.
Released AP Statistics questions and scoring guides are optional. Older exams use the earlier framework, so check alignment before selecting parts. All practice on this page is original, not official AP material.
Learn → Explore → Practice → Review is informed by the IES learning guide. This implementation has not yet been evaluated with learners.
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