Decision science personal statement example

Why do you want to study this course or subject?

What drew me to decision science was noticing how often a sensible-looking choice turns out badly for reasons nobody wrote down. In Economics we studied expected utility and then spent a lesson on why people buy extended warranties anyway; in Further Maths I was learning probability distributions that described exactly the situations my Economics class treated as puzzles about human stubbornness. I wanted the subject that sits between the two, where you build a model of a decision, admit what you do not know, and then ask whether the answer is robust enough to act on.

My EPQ pushed this further. I wrote about whether penalty takers should randomise their placement, starting from the idea of a mixed strategy in a simple two-player game and then comparing it with published summaries of where penalties actually go. The interesting part was not the maths but the gap: goalkeepers are not indifferent, takers have strong feet, and a strategy that is optimal on paper may be unfamiliar enough to be executed badly. Deciding what to leave out of the model taught me more than the calculations did.

Reading Reckoning with Risk by Gerd Gigerenzer changed how I present numbers. His point that the same risk stated as a relative change or as natural frequencies produces different judgements is not a trick of arithmetic; it is a fact about communication that anyone offering analysis has to take seriously. I want to study a degree that keeps both halves together, the optimisation and statistics on one side and the behaviour and framing on the other, because I am more interested in decisions that get taken than in answers that are merely correct.

How have your qualifications and studies helped you to prepare?

I am studying Mathematics, Further Mathematics and Economics at A level, which between them cover most of the groundwork I expect to need. Statistics has been my strongest area: hypothesis testing made much more sense once I stopped treating it as a recipe and started asking what evidence would change my mind. Decision mathematics in Further Maths introduced me to algorithms for shortest paths, critical path analysis and linear programming, and I was surprised how quickly a small problem becomes impossible to solve by inspection. That module is the closest thing school offered to operational research, and it is the reason I looked for degrees in this area rather than pure mathematics.

Economics has been useful in a different way. Coursework on cost-benefit analysis of a local transport scheme forced me to think about which effects can honestly be given a number, and which are being quantified because a spreadsheet demanded a figure. I now read published appraisals with more attention to the assumptions listed near the back.

Outside the syllabus I taught myself enough Python to be dangerous: I wrote a short Monte Carlo script for my EPQ that simulated repeated penalty rounds under different strategies, and I learned the hard way that a simulation reproduces whatever assumptions you feed it. I also worked through the early chapters of a first-year statistics textbook borrowed from the school library to see how matrices and probability fit together. I attended two online lecture series on data and modelling run for sixth formers, which gave me a clearer sense of how much of the work is data cleaning and problem definition rather than analysis.

What else have you done to prepare outside of education, and why are these experiences useful?

For two years I have looked after fixtures and squads for a five-a-side club that runs three teams in a Sunday league. It sounds administrative, and much of it is, but it is genuinely a scheduling problem with constraints: two pitches, players who share lifts, a goalkeeper who works alternate weekends, and a rule that everyone gets a reasonable share of game time. I moved us from a group chat to a shared spreadsheet that tracks availability and minutes played, which cut the weekly arguments considerably and meant I could see patterns, such as one team consistently short in the last month of the season. Explaining to people why the fair allocation is not the one they wanted has been better training in communicating an analysis than any exercise I have been set.

On Saturdays I work on the returns desk of a hardware shop. Customers arrive annoyed, the policy has exceptions, and my job is to reach a decision quickly with incomplete information about what actually happened to the item. I have learned to ask the two or three questions that most change the outcome rather than working through everything in order. I also spotted that a particular cordless drill came back often enough to be worth mentioning to the manager, who checked the supplier's batch and stopped stocking that model; a small thing, but it came from paying attention to a pattern in records nobody was reading.

I was given the sixth form's award for contribution to the maths department after a year of helping at Year 9 support sessions, where explaining probability to someone convinced that a coin is due to land heads has taught me not to assume intuitions are shared. I play badminton twice a week, mostly badly, and read a fair amount of popular science. Together these have built the habits I want to bring to a degree: tidy records, patience with messy information, and a willingness to say what a model cannot tell you.

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