Is a science IA harder than a humanities IA?

A science IA often feels harder at the start because the method can break in real life: equipment fails, results are noisy, and time pressure makes re-testing painful. A humanities IA often feels easier at the start because you can always “read more,” but that is also the trap; it can turn into endless research without a clear line of argument. Difficulty depends less on the subject group and more on how quickly you narrow your research question to something feasible. If you choose a manageable question and run a pilot (science) or do a mini source evaluation early (humanities), both become predictable projects. Examiners reward clarity and control in both types of IA, just in different forms. The “harder” IA is usually the one that started vague.

Can my IA include both qualitative and quantitative evidence?

Yes, and in many subjects it’s normal. In a science IA, you might include qualitative observations (color change, behavior notes) alongside measurements, but the scoring weight still tends to favor quantitative processing and interpretation. In a humanities IA, you might use quantitative data (economic indicators, survey results, demographic tables), but you still need to interpret what the numbers mean and evaluate the data source’s reliability. The safest rule is to match the evidence style to what the rubric rewards for your subject. If you’re unsure, compare two high-scoring exemplars in the Coursework exemplars library and note what actually earns the analysis and evaluation marks. A mixed-evidence IA works when you explain why each evidence type is necessary, not when you include both just to look sophisticated.

How detailed should methodology be in each IA?

In a science IA, methodology detail is a scoring asset because replicability is part of the scientific logic: quantities, timing, control variables, equipment precision, and how you reduced error should be clear. You do not need to write a novel, but you do need enough specificity that a reader could repeat your approach without guessing. In a humanities IA, methodology is less about step-by-step procedure and more about research design: why you selected certain sources, how you define key terms, what perspective or theory guides your analysis, and what limitations that creates. Humanities students often under-write methodology because it feels “obvious,” but examiners like seeing conscious choices. In both cases, the goal is to show control: control of variables in a science IA, control of selection and interpretation in a humanities IA. If your methodology section feels thin, use RevisionDojo’s AI Chat to ask what a marker would need to see to trust your process.

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Science vs Humanities IA: Key Differences

• 19 Jul 2026•11

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Science vs Humanities IA: Key Differences

• 19 Jul 2026•11

If you have an IA due in two subjects, you learn a quiet truth fast: the word “IA” stays the same, but the work feels like two different sports.

In sciences, your IA is often a controlled struggle against variables, equipment, and measurement noise. In humanities, your IA is a controlled struggle against bias, interpretation, and the temptation to summarize instead of argue. Both can earn top marks. Both can also swallow weeks if you start without a plan.

Science vs Humanities IA comic: two different workouts

This guide breaks down the key differences between a science IA and a humanities IA, what examiners tend to reward, and how to build a workflow that keeps you sane while you prepare for exams. If you want models to copy the structure of (not the topic), start with IB Coursework Examples: IA, EE and TOK Exemplars.

Quick checklist: what changes between a science IA and a humanities IA

Use this like a pre-flight check before you commit to a topic.

Evidence: data (science) vs sources (humanities)
Method: replicable procedure (science) vs research strategy + justification (humanities)
Analysis: trends/statistics (science) vs interpretation/argument (humanities)
Evaluation: limitations/errors (science) vs reliability/bias/limitations (humanities)
Writing voice: precise and technical (science) vs persuasive and analytical (humanities)

When you’re unsure, compare your draft to high-scoring examples in the Coursework exemplars library. Seeing what a “7-level” IA looks like often saves a week of guessing.

What a science IA is really testing

A science IA (Biology, Chemistry, Physics, ESS) usually looks like an investigation: you propose a focused research question, design a method, collect data, process it, analyze it, then evaluate the quality of what you did.

But under the surface, examiners are often asking one main thing: Can you think like a scientist with the time and tools available to a student? That means your method isn’t filler. It is the backbone.

In practice, a strong science IA usually includes:

A sharply defined research question tied to measurable variables
A clear rationale for your approach
A method someone else could replicate
Controlled variables (and evidence you actually controlled them)
Raw + processed data (tables/graphs) with correct units and uncertainty where relevant
Analysis that goes beyond “the graph goes up”
Evaluation that proposes specific improvements, not generic ones

If you’re also prepping for exams, the science IA can help you study. After you finish your investigation, you can drill the related content with Questionbank practice and clean up weak theory using the relevant subject hub (for example, IB Physics Resources).

What a humanities IA is really testing

A humanities IA (History, Geography, Economics, Psychology, etc.) is less about apparatus and more about judgment. You’re building a case, selecting evidence, and showing you can evaluate it, not merely repeat it.

A high-scoring humanities IA typically includes:

A narrow research question that forces analysis (not a timeline)
A justified selection of sources or a clear case-study strategy
Context that supports the argument without becoming a textbook chapter
Sustained analysis with multiple perspectives
Explicit evaluation: bias, limitations, representativeness, methodology limits
A conclusion that is earned by reasoning, not announced by confidence

If your notes keep turning into pretty summaries, it helps to shift your revision habits. This article on Effective Note-Taking Strategies for IB Group 3 Subjects is a good reset for humanities-style thinking.

The five key differences that change how you score in an IA

Evidence: numbers vs narratives

In a science IA, your evidence is primarily quantitative: measurements, repeated trials, processed values, graphs, statistics. Even when you include background research, your marks usually rise and fall with the quality of your data handling and interpretation.

In a humanities IA, your evidence is primarily qualitative: documents, perspectives, interviews, case studies, datasets interpreted through theory, and crucially, the credibility of what you chose. Your evidence is only as strong as your evaluation of it.

Methodology: replicable procedure vs defensible selection

Science methodology is about control. If someone repeated your method, would they reasonably get comparable data? Your IA gains strength when you make variables, uncertainty, and procedure decisions visible.

Humanities methodology is about justification. Why these sources? Why this case study? Why this framework? Your IA improves when you show that selection is not random, and that you understand trade-offs.

Analysis: processing vs interpreting

Science analysis often means:

processing raw data accurately
choosing appropriate graphs
identifying relationships
connecting patterns to theory
using statistics where it genuinely supports your claim

Humanities analysis often means:

identifying claims and assumptions
comparing perspectives
applying concepts and theories
weighing reliability and context
building an argument that survives counterargument

A useful exam-prep trick: after writing a paragraph of analysis, test whether it could answer an exam-style prompt. If yes, you’re building transferable skill. If not, you may be drifting into description.

Evaluation: error bars vs bias

Science evaluation asks: what weakened your conclusion, and how would you improve it? Strong science IA evaluations name specific errors (instrument precision, confounding variables, sample issues) and propose realistic fixes.

Humanities evaluation asks: what weakens the trustworthiness of your evidence and reasoning? Strong humanities IA evaluations discuss bias, representativeness, and limitations in scope. They show how those limits shape the conclusion.

Writing style: technical clarity vs argumentative clarity

Science writing is usually compact and objective. You earn marks by being unambiguous: correct terminology, units, labeled tables, and conclusions linked directly to results.

Humanities writing is still precise, but it must persuade. The best humanities IA writing feels like a guided tour through reasoning: claim, evidence, explanation, evaluation, and a clear sense of what would change your mind.

Two examples of strong IA angles (one science, one humanities)

A strong science IA angle might be:

“How does caffeine concentration affect the heart rate of Daphnia?”

It forces controlled variables, repeat trials, careful measurement, and analysis that tests a prediction.

A strong humanities IA angle might be:

“To what extent did propaganda posters influence British civilian morale during World War II?”

It forces source evaluation, perspective, context, and a defensible argument instead of a summary of events.

If you want more real examples across subjects, browse the IB Psychology IA Examples page, then jump outward to other subjects from the Coursework exemplars library.

Common mistakes (and how to avoid them fast)

Common science IA mistakes

Your research question is measurable but not manageable (too many variables, too much equipment dependence)
Too few trials to trust the trend
Beautiful graphs, thin interpretation
Evaluation that says “human error” instead of naming the actual weakness

Fix: before you run the full experiment, do a mini pilot and ask RevisionDojo’s AI Chat what variable control or data processing will likely become your limiting factor. Then refine.

Common humanities IA mistakes

The research question is broad enough to become a history textbook
Sources are collected, not evaluated
Paragraphs summarize what happened rather than argue what it means
Conclusion is certain, but the reasoning is not

Fix: outline your argument as bullet-point claims first. Then add evidence and evaluation under each claim. If you can’t add evaluation, you don’t yet have analysis.

How to use RevisionDojo to build a calmer IA workflow

A good IA is built in loops: draft, feedback, revise, repeat.

RevisionDojo supports those loops without scattering your attention:

Coursework Library: start with coursework exemplars to internalize structure
Grading tools: get criterion-aware feedback faster, especially on evaluation and analysis
Study Notes: anchor your understanding inside the syllabus via subject hubs like IB Economics Resources
Flashcards: turn recurring mistakes (definitions, limitations, command terms) into daily retrieval
Questionbank: reinforce the same skills that your IA needs, but in exam format via Questionbank practice
Mock Exams + Predicted Papers: keep exam readiness moving while your IA takes time. Start with IB Predicted Papers by IB Examiners (Free) and use the strategy in IB Predicted vs Specimen Papers: What They Mean.

Conclusion: one IA, two mindsets

A science IA rewards careful measurement, replicable method, and honest evaluation of error. A humanities IA rewards thoughtful source choice, sustained argument, and evaluation of bias and limits. The best approach is to treat them as two mindsets, not two templates.

If you want your IA to improve your exam performance instead of competing with it, build a loop: study the structure in RevisionDojo’s coursework exemplars, draft with intention, use Grading tools for fast feedback, and keep exam skills sharp through the Questionbank and Predicted Papers. Your future self will feel the difference.

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If you have an IA due in two subjects, you learn a quiet truth fast: the word “IA” stays the same, but the work feels like two different sports.

Quick checklist: what changes between a science IA and a humanities IA

Use this like a pre-flight check before you commit to a topic.

Evidence: data (science) vs sources (humanities)
Method: replicable procedure (science) vs research strategy + justification (humanities)
Analysis: trends/statistics (science) vs interpretation/argument (humanities)
Evaluation: limitations/errors (science) vs reliability/bias/limitations (humanities)
Writing voice: precise and technical (science) vs persuasive and analytical (humanities)

When you’re unsure, compare your draft to high-scoring examples in the Coursework exemplars library. Seeing what a “7-level” IA looks like often saves a week of guessing.