When it comes to the IB Math Internal Assessment (IA), collecting quality data is half the battle. A strong IA isn’t just about solving equations—it’s about gathering reliable, relevant, and well-explained data that directly supports your research question.
In this guide, we’ll walk through the top mistakes to avoid in IB Math IA data collection and explain exactly how to avoid them. Whether you’re starting your IA or reviewing a draft, this list will help you improve your approach and maximize your marks.
Mistake #1: Using Insufficient or Limited Data
Collecting too few data points is one of the most common—and damaging—errors in Math IAs.
Why It Hurts:
- A small sample size (e.g., fewer than 30–50 data points) weakens statistical reliability.
- It limits the depth of analysis, patterns, and conclusions you can draw.
- Your examiners may see it as laziness or poor planning.
How to Fix It:
- Aim for 60–100 data points whenever possible.
- Use tools or surveys that automate collection (e.g., Google Forms, Desmos exports, or real-world datasets).
- If using smaller sets (e.g., experimental physics), clearly justify the limitation and increase mathematical depth elsewhere.
Mistake #2: Over-Reliance on Secondary Data
Secondary data is tempting—easy to find, no fieldwork needed. But relying only on it without explanation can hurt your Personal Engagement score.
Why It Hurts:
- The IB values personal connection and effort in IA projects.
