What this subject area covers
This category groups courses that deliberately cross the boundaries of single sciences. The course titles it includes fall into four rough types, and the evidence that suits each one differs:
- Natural sciences: degrees that let students combine two or more of biology, chemistry, physics, earth sciences and sometimes mathematics or materials. The statement needs to show genuine interest in more than one discipline, not one favourite subject with others mentioned as filler.
- Applied sciences: courses oriented towards using scientific principles on practical problems such as materials, fibres, food, environmental monitoring or analytical testing. Evidence of thinking about how a result is measured, scaled up or made reliable carries more weight here.
- Complexity science and engineering: usually postgraduate or advanced study of systems whose behaviour emerges from many interacting parts, such as networks, ecosystems, traffic, epidemics or markets. Mathematical modelling and computing are central.
- Social sciences within this grouping: where a course sits here, it is likely to treat human behaviour with quantitative or scientific methods. If your course is mainly theoretical, political or philosophical, the neighbouring liberal arts or philosophy, politics and economics areas are a closer fit, and your evidence should reflect that.
Check the actual structure of each course you apply to. A natural sciences course where you choose modules freely needs different emphasis from an advanced science honours course with a research strand, or an applied course with a placement year.
Showing interest across disciplines rather than in one
The most common weakness in these statements is that they read like a chemistry or biology statement with the word “interdisciplinary” added. The useful test is whether you can name a specific question that one discipline alone cannot answer well, and explain what each discipline contributes.
Examples of the kind of connection that works:
- Noticing in A level or equivalent biology that enzyme behaviour depends on concepts from chemistry lessons on rates and equilibria, then reading further on how temperature affects both.
- Seeing that a physics topic such as diffusion or wave behaviour reappears in geography, biology or medicine, and asking how the same mathematics describes different systems.
- Finding that a question about climate, soils or water quality needed chemistry, earth science and statistics together.
Explain the link in your own words and say what remained unclear to you. One well-understood connection is more convincing than a list of fields you would like to combine.
Evidence suited to each branch
Natural sciences
Choose evidence that shows you can work at the level of your current courses in at least two sciences. Practical write-ups, an extended project, olympiad or challenge papers, or follow-up reading on a topic that interested you are all suitable. If you are undecided about your eventual specialism, say so honestly and explain what you want to test by studying more than one; flexibility is a reasonable motive if it rests on specific interests.
Applied sciences
Show that you think about application: accuracy, cost, safety, materials behaving outside ideal conditions, or how a laboratory result becomes a product or process. A school practical where your results differed from the expected value is useful if you analyse why. Reading about a real material or process is useful, for example how fibres are tested for strength or how water is treated. Visits and work experience help if you describe what was measured and why, not just that you attended.
Complexity science and quantitative systems work
Evidence of modelling is the strongest material: a simulation you coded, even a simple one, a spreadsheet model of population growth or disease spread, or work with real datasets. Describe the assumptions you made and where the model failed. For postgraduate applicants, give the methods from your previous degree such as differential equations, network analysis, agent-based modelling or statistical inference, and the type of problem you want to apply them to.
Quantitative social science within this grouping
Show interest in measuring human behaviour carefully: survey design, bias in samples, the difference between correlation and causation, or how a public health or environmental question involves both natural processes and human decisions. Avoid relying only on opinion about social issues; the scientific angle is what places you in this area.
Accessible preparation
None of these are requirements; choose what genuinely interests you and that you can reflect on.
- Extending a school practical: repeating an experiment with a changed variable, or researching why your results deviated. This shows experimental thinking, though not research experience in a professional sense.
- Simple programming or modelling: free tools such as Python or a spreadsheet can model decay, predator and prey populations or random walks. This shows a willingness to formalise ideas; be clear about the scale of what you built.
- Reading beyond the syllabus: popular science books, review articles or science journalism. Mention a specific argument or result and your response to it, not just the title.
- Public datasets: weather, air quality or public health statistics can be analysed at home. This shows handling of real, messy data.
- Citizen science projects: classifying images, recording wildlife or monitoring local conditions. These show contact with how data are gathered at scale, but your contribution is usually small, so describe what you learned about the method rather than claiming a research role.
Using experience that is not obviously scientific
Many applicants have no laboratory placement. Ordinary experience can be relevant if you identify the scientific idea in it and do not overstate it.
- Kitchen or food work: temperature control, food safety procedures and why certain processes prevent spoilage relate to microbiology and chemistry. This shows you noticed science in practice; it does not show knowledge of food science beyond what you went on to read.
- Retail or warehouse jobs: stock flows, queues and delivery scheduling are systems questions relevant to complexity and modelling. Useful if you thought about patterns or bottlenecks; not evidence of mathematical skill unless you analysed them.
- Caring responsibilities: managing medication timings or following changes in someone’s condition can lead to genuine questions about pharmacology or physiology. Mention only what you are comfortable sharing, and keep the focus on the scientific question it raised rather than on medical expertise you do not have.
- Gardening, farming or outdoor hobbies: soil, plant growth, weather and ecology offer real observations. A controlled comparison you tried, such as different growing conditions, is stronger than general enjoyment of nature.
- Repairing bikes, electronics or textiles: practical work with materials and mechanisms suits applied sciences. Explain the principle behind a fault or material choice.
- Gaming or online communities: rarely relevant unless you analysed something, such as emergent behaviour in a simulation game or statistics from play.
What useful reflection looks like
Reflection in a science statement means reasoning about evidence and method. Strong reflection usually does at least one of these:
- States what you expected, what happened and how you explained the difference.
- Identifies an assumption or limitation in an experiment, model or article.
- Shows how one idea changed your understanding of another subject.
- Names the next question you would want to investigate and why.
Compare “I enjoyed a talk on climate modelling” with an account of how the speaker handled uncertainty in cloud behaviour and how that related to error analysis in your own practicals. The second shows thinking; the first only shows attendance.
Pitfalls specific to this subject area
- Vagueness about combinations: saying you like “all of science” without naming the disciplines and the questions linking them.
- Treating interdisciplinarity as avoiding a decision: if indecision is part of your reason, pair it with specific interests you want to test.
- Listing big topics: climate change, quantum computing or artificial intelligence named without any explanation of the science you understand.
- Overclaiming: describing a short visit, an online course or citizen science task as research. Describe your actual role accurately.
- Ignoring mathematics: many of these courses use substantial quantitative methods. If you enjoy or are developing mathematical work, show it; do not suggest you hope to avoid it.
- Writing about a career instead of the subject: a goal in industry, research or policy can be mentioned, but most of the statement should concern the science you want to study, since degrees here lead to many different jobs.
- Misplaced social science: if the course is in this grouping, emphasise methods and evidence rather than only views on society.
Notes for postgraduate applicants
For interdisciplinary or complexity science postgraduate study, the emphasis shifts to methods and research direction. Describe your dissertation or projects precisely: the question, the methods, what you found and what you would do differently. Explain which gaps in your first degree the new course addresses, and if you are moving from one field into another, identify the skills that transfer and those you still need to build. Name research areas, not just broad enthusiasm, and only refer to specific staff or groups if you have read their work and can say why it relates to yours.
For general advice on planning, structure and editing, read our personal statement writing guide.