Statistics · Collecting Data · 14 min read · Updated 2026-05-11
Introduction to Planning a Study — AP Statistics
AP Statistics · Collecting Data · 14 min read
1. Overview of Study Planning★☆☆☆☆⏱ 3 min
Introduction to Planning a Study is the opening topic of Unit 3: Collecting Data. It covers all core preliminary decisions researchers make before collecting any data, answering: what group do we want to learn about, what characteristic are we measuring, and what approach will we use to get reliable data?
This topic is tested on both multiple-choice (MCQ) and free-response (FRQ) sections: expect 1-2 direct MCQ questions, and partial credit on FRQ questions asking you to evaluate or describe a study. Standard notation conventions used throughout this topic (and the rest of the course) follow consistent rules:
$N$ = population size, $n$ = sample size
Greek letters for population parameters
Latin letters / hats for sample statistics
2. Populations, Samples, Parameters, and Statistics★★☆☆☆⏱ 4 min
Exam tip: Always define these terms in the context of the problem, not just with generic definitions. AP exam readers require context for full credit.
3. Census vs. Sampling★★☆☆☆⏱ 3 min
Censuses are only appropriate when the population is small and easy to access, or when 100% accurate data for every individual is required. Sampling is far more common for four key reasons: it saves time, it is much cheaper, it is required for destructive testing (testing destroys the item), and a well-designed random sample produces very accurate estimates even for large populations. A common misconception is that censuses are always more accurate: in practice, censuses often have high nonresponse or measurement error that makes them less reliable than a good sample.
Exam tip: If an AP question asks whether a census is appropriate, always check for destructive testing first — it is the most frequently tested scenario.
4. Observational Studies vs. Experiments★★★☆☆⏱ 4 min
The key difference: experiments assign treatments, while observational studies only observe existing behavior or characteristics. The critical consequence is that only well-designed experiments can support causal (cause-and-effect) conclusions; observational studies can only show association, due to the risk of confounding.
Exam tip: A study can only support a causal conclusion if it is explicitly described as a randomized experiment with random treatment assignment.
Common Pitfalls
Why: Students mix up Greek/Latin notation and confuse whether the value describes the entire population or just the sample
Why: Students assume more data is always better, ignoring practical constraints that make a census impractical or less accurate
Why: Students assume any grouping means an experiment, but grouping by existing characteristics is not treatment assignment
Why: Students forget association does not equal causation, and assume any comparison group allows causal conclusions
Why: Students confuse the sample (the group you actually measure) with the population (the group you want to learn about)