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12 multiple-choice questions and 28 flashcards on Random Sampling and Inference, about 8% of the 7th Math: Statistics & Probability bank. Every one carries a written rationale.
Random Sampling and Inference is one of 5 chapters in CoStudy's 7th Math: Statistics & Probability bank, and it holds 12 of the bank's 150 multiple-choice questions — roughly 8% of the total. That proportion is not arbitrary: chapters follow the certifying body's published exam outline, and the number of questions in each is set by that domain's published weight, so the share of your practice time this chapter takes matches the share of the real exam it accounts for.
Studying by chapter is worth doing once you have a diagnostic score. A single overall percentage tells you whether you are close; it does not tell you which domain is dragging. Working a weak chapter in isolation, and re-testing it in isolation, is the fastest way to move a score that has stalled — and it is why the mock exams in CoStudy report by domain rather than as one number.
3 questions drawn from this chapter, with the full rationale shown — the controlling principle behind the right answer, and why each wrong option tempts and fails.
A random sample is one in which:
Answer: A — Every member of the population has an equal chance of being selected
A) Definition of simple random sampling. B/C/D) All introduce selection bias.
A student concludes 'most kids hate broccoli' after asking 4 friends at lunch. The MAIN problem is:
Answer: C — The sample is too small AND not random — can't generalize
Both issues matter: 4 is too few, and friends share traits so the sample is biased. A small non-random sample cannot support a population claim. (A) sidesteps the math; (B/D) irrelevant.
Which is an example of CONVENIENCE sampling (likely biased)?
Answer: D — Surveying only the kids at the front of the cafeteria line
Convenience sampling picks who's easiest to reach, which usually biases the sample. (A/B/C) all describe random methods.
4 cards from the 28 in this chapter.
Sample space for rolling a die?
{1, 2, 3, 4, 5, 6}.
Why is random sampling important?
It avoids bias and gives a sample more representative of the whole population.
Roll 2 dice. P(sum = 7)?
6/36 = 1/6. (Pairs: (1,6), (2,5), (3,4), (4,3), (5,2), (6,1).)
What's a biased sample?
One that systematically favors certain outcomes (and so does not represent the population).
These are a sample. The full Random Sampling and Inference chapter runs 40 items with per-chapter progress tracking, on the web and in the iOS app.
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