<\/p>\nYou’re in the middle of an IB DP research internship \u2014 whether at a local lab, a community organisation, a classroom project, or a virtual placement. By the time you have numbers, logs, or interview transcripts, the challenge becomes how to show what those data mean. Presentation is not decoration: it’s an argument. A carefully chosen chart, a clear caption, and a tidy appendix turn raw material into convincing evidence of inquiry. That matters for supervisors, CAS moderators, and for your own learning record.<\/p>\n
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This guide gives IB students a practical path from messy notes to a polished report or portfolio entry. You\u2019ll find planning checklists, data-cleaning best practices, visualization advice, example phrasing for results, portfolio integration tips, and quick fixes for common problems. Throughout, the emphasis is on clarity, reproducibility, and honest interpretation \u2014 skills that make your internship stand out in any assessment cycle.<\/p>\n
Clarity does several things at once. First, it respects your audience: a busy assessor should be able to find your main claim, how you measured it, and why it matters without wading through irrelevant detail. Second, clarity enforces discipline: organizing your data and methods forces you to confront assumptions, potential biases, and gaps in reasoning. Third, clear presentation protects academic integrity. When figures and tables are easy to check against raw data and code, it\u2019s simple to reproduce results or correct honest mistakes.<\/p>\n
In practical terms, clear presentation makes your learning visible. The IB values reflection and evidence of skill development. When your appendix shows a clear workflow \u2014 raw data, cleaning steps, code or formulae, and final visuals \u2014 you demonstrate more than a result: you demonstrate method. That is precisely what supervisors and CAS moderators want to see.<\/p>\n
A short data map is a small document that saves huge amounts of time. Think of it as a one-page summary for anyone who opens your folder: what you asked, what you measured, and where the files live. It can be part of your portfolio and a good way to start your methods section.<\/p>\n
This document helps you answer “What did you do?” in one page \u2014 a huge win for assessors who skim for clarity.<\/p>\n
Data cleaning is where you show care. It\u2019s also the place many students rush and regret later. The core principle is transparency: record every decisive step and keep the raw data untouched in a separate folder.<\/p>\n
Finally, version control matters even for small projects. Save iterative copies and make short notes about what changed in each version. If you used scripts (R, Python, JASP macros), include them; if not, a step\u2011by\u2011step spreadsheet log will suffice.<\/p>\n
Charts are tools with different purposes. A common student mistake is to create a chart because it looks impressive rather than because it answers a question. Use the following reference \u2014 and remember that a table is a visualization too when your aim is precise numbers.<\/p>\n
| Visualization<\/th>\n | Best for<\/th>\n | What it reveals<\/th>\n | Practical tip<\/th>\n<\/tr>\n<\/thead>\n |
|---|---|---|---|
| Bar chart<\/td>\n | Comparing categories<\/td>\n | Rank order and contrasts between groups<\/td>\n | Sort bars by value for clearer comparison<\/td>\n<\/tr>\n |
| Line chart<\/td>\n | Trends over time or ordered sequences<\/td>\n | Direction, rate of change<\/td>\n | Use consistent intervals and label time units<\/td>\n<\/tr>\n |
| Scatter plot<\/td>\n | Relationship between two numeric variables<\/td>\n | Correlation, clusters, and outliers<\/td>\n | Add a fitted line and report correlation in caption<\/td>\n<\/tr>\n |
| Histogram<\/td>\n | Distribution of a variable<\/td>\n | Skewness, modality, spread<\/td>\n | Explain bin widths; small samples may mislead<\/td>\n<\/tr>\n |
| Boxplot<\/td>\n | Comparing distributions<\/td>\n | Median, quartiles, and outliers<\/td>\n | Use alongside a small table of descriptive stats<\/td>\n<\/tr>\n |
| Heatmap<\/td>\n | Correlations or matrix data<\/td>\n | Patterns across many variables<\/td>\n | Choose a color scale readable in grayscale<\/td>\n<\/tr>\n |
| Table<\/td>\n | Precise numbers and small samples<\/td>\n | Exact results and values that must be verified<\/td>\n | Keep columns to the essentials; report units<\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/div>\n As a rule of thumb: use visuals to show patterns and tables to show numbers. If a reader would wonder “how much?” use a table; if they would wonder “what direction or pattern?” show a chart.<\/p>\n Design details: labels, color, scale, and accessibility<\/h3>\nA clean design is readable design. These specifics make your visuals robust whether they are printed for a supervisor or viewed on a phone.<\/p>\n
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