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NotesMath AI HLTopic 4.12Sampling, reliability & validity
Back to Math AI HL Topics
4.12.12 min read

Sampling, reliability & validity

IB Mathematics: Applications and Interpretation • Unit 4

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Contents

  • The five sampling methods
  • Bias, reliability and validity
You can't survey everyone — so sample fairly: A school has 1200 students but you only have time to survey 60. The whole 1200 is the population; the 60 you actually ask are the sample. The goal is a sample that looks like the population in miniature, so your conclusions generalise.

How you pick those 60 is the sampling method — and a careless method quietly skews every number that follows.

Which method gives a fair picture, and which one is fastest but biased?

The five methods at a glance

  • Simple random — everyone has an equal chance (names from a hat / GDC random numbers)
  • Systematic — order the list, pick every kᵗʰ person from a random start
  • Stratified — split into groups (strata), sample each in proportion to its size
  • Quota — like stratified but the interviewer chooses who fills each quota (non-random)
  • Convenience — just ask whoever is easiest (most biased, least representative)

IB-style question — stratified sample

A college has 600 Year 12 and 400 Year 13 students. A stratified sample of 50 students is taken across the two year groups.

Find how many Year 12 students should be in the sample.

Step by step

  1. Stratified means each group is sampled in proportion to its size. First the total population.
  2. Year 12's share of the population, times the sample size.
  3. (Check: Year 13 gives 400/1000 × 50 = 20, and 30 + 20 = 50.)

Final answer

30 Year 12 students (and 20 Year 13). Stratifying keeps the sample's mix the same as the college's.

Two different ways data can go wrong: Imagine a bathroom scale.

If it reads 70.1, 70.0, 70.2 kg every time you step on it, it is reliable — it gives the same answer consistently.

But if your true mass is 65 kg, that scale is not valid — it is reliably measuring the wrong thing.

Reliability = consistency (small random error). Validity = measuring what you intend (no systematic error / bias). A measure can be reliable but not valid; to be trustworthy it must be both.

IB-style question — spot the bias

A researcher wants the average daily screen-time of all teenagers in a town. She stands outside a gaming cafe at 6 pm and asks everyone who walks out.

Identify the sampling method and explain why the estimate is biased.

Step by step

  1. She asks whoever is easiest to reach at one place and time — no random selection.
  2. People leaving a gaming cafe are far heavier screen-users than typical teenagers.
  3. So the mean screen-time will be pushed up — a systematic over-estimate.

Final answer

Convenience sampling. The sample over-represents heavy screen-users, so the estimate is biased upwards (too high) — it is not a valid measure of all teenagers.

Reliable vs valid in one line: Reliable but not valid → a clock that's always exactly 10 minutes fast (consistent, but wrong).

Valid but not reliable → a clock with the right average time but that jumps around randomly.

You want both: consistent AND correct.

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A company has 900 employees: 540 in production and 360 in offices. A stratified sample of 30 is taken. Find how many office employees should be sampled. [2 marks]

Related Math AI HL Topics

Continue learning with these related topics from the same unit:

4.1.1Population and Samples
4.1.2Data Classification
4.1.3Sampling Techniques
4.1.4Data Reliability and Outliers
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