Testable research questions
Turn a broad observation into a specific hypothesis. Define the observations, comparison, and evidence that would change the conclusion.
03 / QUANTITATIVE RESEARCH
Quantitative Research
Evidence before conviction.
Complex data invites compelling stories. My interest is in the work that comes before believing them: asking precise questions, testing assumptions, and understanding what the evidence can actually support.
THE PERSPECTIVE
Find the signal.
I approach quantitative research as a discipline of careful comparison. A pattern can be interesting without being useful, and an attractive historical result can hide fragile assumptions. I care about making those distinctions visible, so the next experiment is better informed than the last.
WHERE I FOCUS
What I work on.
Data exploration
Examine the shape, completeness, and context of a dataset. Look at how a finding changes across samples and assumptions before treating it as a stable result.
Research tooling
Create inspectable experiments and visual tools that make the relationship between inputs, assumptions, and outputs easier to understand.
HOW I APPROACH IT
Think clearly.
Move deliberately.
Make the question useful. Keep the work inspectable. Let the result inform the next decision.
- 01
State the question
Record the hypothesis and the comparison before experimenting, including what would count as evidence against the idea.
- 02
Make the experiment inspectable
Keep the data preparation, parameters, and calculations reproducible so a result can be checked and revisited.
- 03
Challenge the interpretation
Explore sensitivity, alternative explanations, and the limits of the sample. Separate a research observation from a claim about future performance.
THE EXPERIENCE BEHIND IT
Background & current focus.
Quantitative research is a current area of focus, informed by my background in analytics, technical problem-solving, and building tools. The small illustration below explains one concept using synthetic data. Published research or independently validated investment results are not presented here.
Explore my background A SIMPLE ILLUSTRATION
How smoothing
changes a chart.
These two controls show a basic trade-off: a wider moving average smooths variation but responds more slowly to changes.
Adjust the noise and averaging window to compare the two lines.
SMOOTHING DEMOSYNTHETIC DATA
Raw observationsMoving average
HIGHLOW
OBSERVATION 01120
Synthetic data · A demonstration of smoothing, not a trading model. A wider window reduces noise but introduces lag.
THE NEXT CONNECTION
Let’s put the thinking to work.
Have a challenge connected to quantitative research? Tell me about the problem, the people involved, and what you’d like to achieve.