Your Math IA is only as strong as the data behind it.
Whether you’re modeling, analyzing trends, or running regressions, examiners expect your data to be relevant, reliable, and well-organized.
Poor data — missing values, inconsistent units, or unclear presentation — can drag down even the best mathematical work.
With RevisionDojo’s IA/EE Guide, Data Collection Tools, and Exemplars, you’ll learn exactly how to gather and structure data that supports your math, strengthens your argument, and impresses examiners.
Before collecting or organizing your IA data:
- Identify what kind of data your topic requires.
- Decide between primary and secondary sources.
- Collect enough data to show meaningful patterns.
- Clean and organize data in a clear, consistent format.
- Use RevisionDojo’s Data Tools to visualize early trends.
Start with your aim or research question.
Ask yourself:
“What specific variables will help me answer this question mathematically?”
For example:
- Investigating running speed? → Time and distance.
- Modeling bacteria growth? → Population count over time.
- Studying economics? → Revenue and cost data.
RevisionDojo’s Data Planning Worksheet helps you list dependent and independent variables before collecting anything.
Both options can work, but they serve different purposes:
- Primary Data: Collected firsthand — shows initiative and engagement.
- Secondary Data: Gathered from published or online sources — saves time and ensures larger sample sizes.
RevisionDojo’s Data Source Library includes verified examples (sports stats, climate data, financial series) that IB students can use safely and cite correctly.
To earn high marks in Use of Mathematics and Reflection, your data must be reliable.
Check for:
- Consistent measurement units.
- Adequate sample size (usually 20–40 data points minimum).
- No fabricated or unrealistic values.
RevisionDojo’s Data Integrity Checklist helps you confirm quality before analysis begins.
Consistency prevents mathematical errors.
If one dataset uses meters and another uses kilometers, convert them early.
Same with currency, time units, or frequency intervals.
RevisionDojo’s Data Cleaning Tool highlights unit mismatches and suggests standardization before modeling.
Present your data in clean, labeled tables before creating graphs.
Each column should have:
- Variable name.
- Units of measurement.
- Values listed in logical order (e.g., ascending time or value).
RevisionDojo’s IA Table Formatter automatically aligns columns, adds headers, and generates IB-compliant tables ready for insertion into your report.
Before choosing a model, visualize your data using scatterplots, histograms, or line graphs.
This helps reveal patterns, outliers, and relationships.
RevisionDojo’s Data Visualization Module lets you test multiple graph types quickly to see which best represents your dataset.
Example:
“A scatterplot of height versus time revealed a nonlinear pattern, suggesting a quadratic model.”
If you’re using secondary data, always cite it clearly.
Include:
- Dataset title.
- Source website or publication.
- Date accessed.
RevisionDojo’s Citation Generator formats references automatically in IB-approved style — saving hours of editing later.
Outliers shouldn’t automatically be deleted — they often reveal something interesting.
Instead, identify and analyze them:
“The outlier at t = 5 seconds may represent a measurement error or a unique event worth separate consideration.”
RevisionDojo’s Outlier Detector flags anomalies visually and prompts reflection questions for IA discussion.
Always store your datasets in one folder with clear file names (e.g., “IA_Data_Raw.csv,” “IA_Cleaned.xlsx”).
This saves time later and allows easy updates if you adjust your model.
RevisionDojo’s IA Project Dashboard includes secure cloud storage and version tracking for your data files.
Examiners expect you to acknowledge imperfections in your dataset.
Write short reflections like:
“While the dataset provided sufficient range, limited sample size may affect generalizability.”
RevisionDojo’s Reflection Prompts help you express these insights professionally, linking data evaluation directly to your final reflection section.
1. How many data points should my IA include?
Generally, 20–40 points is ideal — enough to show trends without overwhelming your analysis.
2. Can I use data from the internet?
Yes, but you must cite it clearly and verify that it’s reliable and relevant to your topic.
3. Do I lose marks if my data is imperfect?
Not if you discuss the imperfections thoughtfully. Reflection on limitations actually strengthens your mark.
Good data is the foundation of a strong IA.
It supports your mathematics, strengthens your reasoning, and makes your conclusions believable.
With RevisionDojo’s IA/EE Guide, Data Tools, and Exemplars, you’ll collect, organize, and analyze your data like a professional mathematician — ready for top marks in every criterion.
Start building your IA dataset today.
Use RevisionDojo’s Data Tools and IA/EE Guide to collect, clean, and organize reliable data that makes your exploration stand out.
