Question 1

What is NCEA AS 91264 about?

Accepted Answer

This standard asks you to use statistics to answer a research question by following the full statistical enquiry cycle. You'll work with data samples, create displays, calculate measures, discuss what the data tells you, and write a conclusion about the population you're studying. It's about the whole process—not just calculations, but thinking through why you're doing each step and what it means.

Question 2

What do students need to do for AS 91264?

Accepted Answer

1. Pose a comparison question based on given data and clearly explain what you're investigating 2. Select and justify random sampling methods to collect data from your population 3. Choose appropriate graphs, displays and statistical measures (like averages or medians) and use them correctly 4. Discuss what your sample data shows, including how much variation there is in your estimates and why sample size matters 5. Make a clear inference (conclusion) about the population that is supported by the evidence you found

Question 3

What's the difference between Achieved, Merit, and Excellence for AS 91264?

Accepted Answer

Achieved: You use all parts of the statistical enquiry cycle to answer your question, showing you've calculated measures, made displays, and drawn a conclusion about the population. Merit: You connect each step of your statistical process to the context and the population, backing up your statements with specific data values, statistics, or features visible in your graphs. Excellence: You integrate statistical knowledge with contextual understanding throughout your investigation, reflect on how the process worked, and consider alternative explanations or limitations in what the data tells you.

Question 4

What mistakes do students typically make in AS 91264?

Accepted Answer

1. Not following the full statistical enquiry cycle—skipping steps like discussing sample distributions or addressing sampling variability 2. Making inferences without supporting them with actual evidence from statistics, data values, or visual displays 3. Confusing the sample with the population, or not clearly explaining what population your conclusion applies to

Statistical inference

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