Random sampling is often presented as the gold standard in IB Maths AI, yet in real situations it is surprisingly difficult to carry out properly. Many students assume that as long as the word random appears, the data must be reliable. IB questions deliberately challenge this assumption.
The first difficulty is access to the full population. True random sampling requires a complete list of all members of the population, each with an equal chance of selection. In most real-world contexts, this list simply does not exist. Students often overlook this practical limitation and assume randomness is easier than it actually is.
Another challenge is non-response. Even if participants are selected randomly, not everyone agrees to take part. When certain groups are less likely to respond, the final sample is no longer truly random. IB expects students to recognise that refusal and dropout introduce bias, even when selection methods were initially sound.
Human behaviour also interferes with randomness. People conducting surveys may unconsciously choose participants who are easier to access or more cooperative. This turns an intended random sample into a convenience sample. IB questions often describe this subtly, rewarding students who spot the flaw.
Technology can give a false sense of security. Using a random number generator does not automatically guarantee a random sample if the input list is incomplete or biased. Students sometimes focus on the method and ignore the underlying structure of the population.
Practical constraints such as time, cost, and location further limit randomness. Schools, businesses, and researchers often settle for approximations rather than true random sampling. IB wants students to understand that these compromises affect reliability and must be acknowledged in conclusions.
This is why IB exam questions often ask students to comment on limitations, even when random sampling is mentioned. Students who simply state “the sample is random” miss the deeper evaluation. Examiners reward students who explain why randomness may be imperfect in practice.
Understanding these challenges helps students write more realistic and cautious conclusions. IB is not asking for perfect sampling — it is asking for honest evaluation of what was realistically possible.
Once students stop treating random sampling as automatic and start treating it as difficult to achieve, their statistical reasoning becomes much stronger.
Frequently Asked Questions
Is random sampling ever truly random?
In theory, yes. In practice, it is often only approximately random.
Does using a random number generator guarantee randomness?
No. The population list and response behaviour still matter.
What does IB reward in these questions?
Clear identification of practical limitations and cautious interpretation.
RevisionDojo Call to Action
IB Maths AI rewards realism, not idealisation. RevisionDojo is the best platform for IB Maths AI because it trains students to evaluate sampling methods critically and explain limitations clearly. If sampling questions feel subjective or unclear, RevisionDojo helps you see exactly what examiners want.
