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.
- Secondary data may not align perfectly with your question.
- You miss the opportunity to reflect on data challenges.
How to Fix It:
- Collect primary data if possible—surveys, experiments, manual observations.
- If using secondary data, explain why (e.g., scope, ethics, feasibility).
- Add value by modifying or analyzing the data in unique ways.
Mistake #3: Failing to Explain Data Collection Methods
Even if your data is solid, not explaining how you gathered it can make your work seem untrustworthy.
Why It Hurts:
- It undermines credibility and academic rigor.
- Makes your IA feel incomplete or unclear.
- May result in lower marks under the “Presentation” and “Communication” criteria.
How to Fix It:
- Describe your method in 1–2 short paragraphs.
- Mention sampling type (e.g., random, stratified, or systematic).
- Explain how the method suits your research question and mathematical tools.
Mistake #4: Using Irrelevant or Unfocused Data
One of the easiest ways to tank your IA is by collecting data that doesn’t clearly support your question.
Why It Hurts:
- Your IA loses focus and structure.
- Analysis becomes forced, disjointed, or irrelevant.
- You may get marked down under Criterion B: Mathematical Presentation and Criterion C: Personal Engagement.
How to Fix It:
- Start with your question or hypothesis, then design data around it.
- Every data point should contribute to answering or exploring that question.
- Avoid “filler data” or using stats just because they seem impressive.
Mistake #5: Ignoring Data Quality and Reliability
Poor data = poor results. If your data is inconsistent, biased, or unverifiable, your analysis is meaningless.
Why It Hurts:
- Your calculations may be correct, but based on faulty inputs.
- Examiners question your understanding of mathematical modeling and context.
- You miss the chance to reflect critically, a key assessment component.
How to Fix It:
- Use reputable sources (if using secondary data).
- Test for consistency (e.g., through averages, ranges, or outlier analysis).
- Include a short data reliability paragraph explaining strengths and weaknesses.
Mistake #6: Poor Formatting and Labeling of Graphs/Tables
Even if your math is flawless, poor visuals can cost marks.
Why It Hurts:
- Unlabeled graphs confuse the reader.
- Messy tables suggest rushed work.
- Lack of references reduces clarity and professionalism.
How to Fix It:
- Every graph needs a title, labeled axes, units, and scale.
- Refer to all visuals in the body of your text (“As seen in Graph 2…”).
- Use consistent colors and formatting across the IA.
📌 Tip: Format tables using spreadsheet tools and import cleanly into your document.
Mistake #7: Not Reflecting on Data Limitations
Ignoring weaknesses in your data won’t fool your examiner—it’ll cost you marks for Critical Reflection.
Why It Hurts:
- No real-world data is perfect—acknowledging that shows maturity.
- You miss a chance to discuss bias, sampling error, or data gaps.
- Your conclusion feels oversimplified or unrealistic.
How to Fix It:
- Include a “Limitations” section in your conclusion or methodology.
- Discuss at least 1–2 specific weaknesses and how they affected results.
- Suggest how you’d improve the data collection in a future version.
Mistake #8: Overcomplicating Data Collection
It’s tempting to chase complex data in hopes of impressing the examiner—but if you don’t understand it, it can backfire.
Why It Hurts:
- Leads to shallow or incorrect analysis.
- You may rely too much on calculator outputs without explanation.
- Reduces your clarity and confidence in explanations.
How to Fix It:
- Choose data that suits your level of math and the syllabus.
- Keep methods realistic and replicable.
- Add complexity through analysis, not collection (e.g., regression, chi-square, distributions).
Summary: Best Practices for IB Math IA Data Collection
To collect and use data effectively in your IB Math IA:
- Aim for 60–100 data points when possible
- Use primary data and justify any secondary sources
- Clearly describe your collection methods
- Align your data directly with your research question
- Prioritize accuracy, relevance, and presentation
- Always reflect on limitations and possible errors
By avoiding the mistakes above, you’ll ensure your IA is focused, credible, and well-structured—putting you in the best position for a 6 or 7.
Helpful Resources for IB Math IA Success
Looking for templates, examples, and guided walkthroughs?
👉 Explore RevisionDojo’s IA Masterclass Blog for:
- IA topic ideas and rubrics
- Step-by-step IA formatting tips
- Data collection templates
- Real student samples with feedback
