Why Real Data Makes Your IA Stand Out
Using real-world data shows that your mathematics has meaning.
It demonstrates that you can connect abstract concepts to measurable reality — a skill highly valued in Criterion D (Use of Mathematics) and Criterion E (Reflection).
When done well, real data adds authenticity, depth, and engagement to your IA. But when done poorly — with unclear sources or inconsistent analysis — it can reduce clarity and credibility.
With RevisionDojo’s IA/EE Guide, Data Tools, and Exemplars, you’ll learn how to collect, process, and interpret real data in ways that strengthen both your analysis and your overall score.
Quick-Start Checklist
Before integrating data:
- Choose relevant, high-quality sources.
- Verify that your data supports your IA aim.
- Clean and format it for consistency.
- Analyze trends before applying math.
- Use RevisionDojo’s Data Tools to visualize and validate your dataset.
Step 1: Select Data That Aligns With Your Aim
Your dataset must directly support your research question — not just look interesting.
Example:
“Since this IA investigates the cooling of liquids, temperature data over time was collected at fixed intervals.”
RevisionDojo’s Data Relevance Checker ensures your dataset aligns with your mathematical objective.
Step 2: Use Reliable Sources or Collect Your Own Data
Examiners reward transparency. Mention whether your data is primary (collected yourself) or secondary (from credible sources).
Examples of strong data sources:
