Design an experiment to launch fractional shares
Company: Robinhood
Role: Data Scientist
Category: Analytics & Experimentation
Difficulty: easy
Interview Round: Onsite
## Context
You work on a brokerage/investing app. The team is considering launching **fractional share trading** (users can buy/sell fractions of a stock/ETF instead of whole shares).
## Task
1. **Explain the expected benefits** of fractional shares for the business and for users.
2. Propose an **experimentation plan** to evaluate the launch:
- What is the **primary success metric**?
- What **diagnostic metrics** help explain movement?
- What **guardrail metrics** ensure you don’t harm users or the business?
- Define the **unit of randomization** (user/account/household/etc.) and the treatment/control experience.
- Call out key risks: interference/network effects, novelty effects, seasonality, and any selection bias.
3. **Sample size / power**
- Show how you would estimate the **required sample size** for the primary metric (state assumptions such as baseline rate/variance, MDE,
\(\alpha\), and power).
- If you need to **adjust the sample size mid-experiment**, explain a statistically valid approach.
4. **Constraint scenario**
- If the required sample size is **too large** (you don’t have enough traffic or time), what do you do? Provide at least 3 practical options (design, metrics, variance reduction, ramp strategy, or alternative inference methods) and discuss tradeoffs.
## Output
Provide a structured written plan, including metric definitions, assumptions used in power/MDE calculations, and a final recommendation on whether/how to launch.
Quick Answer: This question evaluates a candidate's competency in experimentation design, product analytics, statistical power and sample-size calculation, causal inference, and risk identification within a brokerage product context.