An example of the written feedback our students receive from tutors after each practice interview.
John A. demonstrates strong intellectual range, genuine curiosity, and clear signs of teachability. His qualitative reasoning is already almost at undergraduate level. The priority for forthcoming sessions is developing basic data analysis skills and training him to pause and articulate his thought process out loud before committing to an answer.
John showed a strong ability to analyse political and philosophical questions by breaking them into their component assumptions, considering multiple perspectives, and reasoning through the consequences of different positions. For instance, when asked whether compulsory voting leads to better democratic outcomes, he began by questioning what “better” means in a democratic context, distinguishing between participation, representation, and individual liberty. He gave a nuanced three-part answer to the question which considered each of these perspectives. This was one of several examples in which John rooted his responses in logical argumentation and critical evaluation rather than reverting to many candidates’ tendency to arbitrarily cite memorised (and often irrelevant) examples.
John showed a consistent open-mindedness and ability to adapt his viewpoint to counter-arguments without fully capitulating to all criticism. This is a sign of high teachability, which is a crucial quality that admissions tutors look for in students. For instance, when discussing whether elected representatives should follow their own judgement or the preferences of their constituents, John initially defended a trustee model of representation. However, when challenged on the democratic legitimacy of allowing politicians to override voters’ wishes, he carefully considered the point, identified the limitations of his initial argument, and developed a more nuanced response that incorporated elements of both perspectives.
John’s quantitative analysis skills require some improvement. When presented with the 2025 Belarus election data (85.70% turnout, 86.82% vote share), he identified the figures as suspicious but struggled to articulate why in statistical terms. He instead jumped to a fairly rushed conclusion about fraudulence that relied entirely on qualitative reasoning. To prepare for future quantitative exercises, John should practise analysing graphs to focus on identifying correlations, distinguishing between what the data demonstrates and what can be inferred from it, and drawing measured conclusions from the evidence.
John’s answers were consistently thoughtful and well-reasoned. However, he occasionally spent longer than necessary developing points that had already been established, which limited the time available to explore additional dimensions of the question. Next time, he should aim to arrive at a fairly clear initial position more efficiently and then use the remaining discussion to test assumptions, engage with counterarguments, and explore implications. Greater concision would allow John to showcase an even wider range of analytical thinking within the interview format.
Dedicate the first 15 minutes of the next two sessions to unseen graphs and tables. Drill a consistent framework: read the axes, identify the claim the data appears to make, list at least two alternative explanations, and note what the data cannot tell us.
Write one 200-word argumentative paragraph per week on a set prompt, to be reviewed by the tutor. The aim is to consolidate John’s natural facility with ideas into tighter, more structurally disciplined written arguments that mirror Oxford tutorial expectations.
Read Chapters 1–3 of Darrell Huff’s How to Lie with Statistics and Tim Harford’s The Data Detective. Focus on developing instincts for what makes a dataset trustworthy or misleading, and practise applying this to real political data.