Does sample size matter in IB Math at all?

Yes, sample size matters in IB Math, but it matters later than most students think. Once your sampling method is reasonably unbiased and representative, a larger sample typically reduces random variation and makes estimates more stable. That’s when you can argue that results are “more reliable” because sampling variability is smaller. But if your method is biased, adding more data mostly increases confidence in the wrong answer. In an exam response, it’s strong to say: size improves precision, method determines validity. That distinction is exactly what examiners look for in explanation marks.

What sampling method does IB Math usually prefer?

In IB Math, simple random sampling is often described as the gold standard because each member of the population has an equal chance of selection. Stratified sampling is also highly valued when you need key subgroups represented in fair proportions. However, IB questions also test realism: sometimes truly random sampling is hard in practice, so you should discuss practical constraints honestly. The best answers name the method, explain why it reduces bias, and mention what could still go wrong (like a poor sampling frame or non-response). If you keep losing marks on wording, practice writing two-sentence evaluations and refine them with RevisionDojo’s AI Chat. Over time, the phrasing becomes automatic.

Can a very large biased sample still be unreliable?

Absolutely, and this is one of the most important lessons in IB Math statistics. Bias is systematic error, so it doesn’t cancel out as you add more observations. A biased method can produce a very consistent estimate that is consistently wrong, which is why large biased samples can look persuasive. In exam questions, this shows up when the prompt gives a huge n but also hints at convenience or self-selection. Your job is to prioritise representativeness over impressive numbers. A top-band conclusion states what the results may apply to, and what they cannot be generalised to.