Statistical inference and simulation
Part of the Quant learning and recruiting path. Read the explanation and example, attempt the task, then open the self-check.
Know what to learn for your role.
From foundations to a work sample, with sources, tasks and self-checks.
Understand
Sample estimates vary; correlation is not causation. Simulations need explicit random mechanisms, sample sizes and seeds. Agreement with theory checks implementation, not the truth of assumptions.
Worked example
A fair coin need not show exactly 50 heads in 100 flips. Deviation alone does not establish unfairness.
Examples and numbers are educational, not employer questions, live market data or hiring criteria.
Practise and keep your output
Simulate 1,000 groups of 100 coin flips and plot head proportions with reproducible settings.
Keep inputs and sources, assumptions, steps, results and uncertainties in your own document. Attempt independently before checking below.
After attempting: answer and self-check
The distribution should centre near 0.5. State seed and sample design, and avoid selecting a convenient run.
Official channels and further reading
Pathline authors these starter lessons. These supplementary field links do not imply provider review or endorsement. Confirm Hong Kong availability and eligibility separately.
- Jane Street · Careers ↗
Separate research, trading and development, then confirm location and eligibility.
- Jane Street · Interviewing ↗
Choose official interview preparation for the relevant role.
- Python · Official tutorial ↗
Programming-language reference. Complete this line’s basic exercises alongside it.