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Apply sequential testing without p-hacking

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a candidate's competency in sequential hypothesis testing, alpha‑spending rules (Pocock vs O’Brien–Fleming), always‑valid inference methods such as mixture‑SPRT and e‑values, and strategies for controlling error rates across multiple metrics.

  • hard
  • Meta
  • Statistics & Math
  • Data Scientist

Apply sequential testing without p-hacking

Company: Meta

Role: Data Scientist

Category: Statistics & Math

Difficulty: hard

Interview Round: Onsite

You want continuous monitoring with early stopping for efficacy or futility. a) Contrast Pocock vs. O’Brien–Fleming alpha‑spending for two interim looks and report the corresponding critical z‑values. b) Explain how mixture‑SPRT or e‑values yield always‑valid inference under arbitrary peeking. c) Describe how you would integrate sequential monitoring with multiple metrics without inflating error rates.

Quick Answer: This question evaluates a candidate's competency in sequential hypothesis testing, alpha‑spending rules (Pocock vs O’Brien–Fleming), always‑valid inference methods such as mixture‑SPRT and e‑values, and strategies for controlling error rates across multiple metrics.

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Meta
Oct 13, 2025, 9:49 PM
Data Scientist
Onsite
Statistics & Math
6
0

Sequential Monitoring With Early Stopping

Context: You are planning a two‑sided hypothesis test with continuous monitoring and early stopping for efficacy or futility. Assume two interim analyses plus a final analysis (3 total looks) at equally spaced information times (t = 1/3, 2/3, 1) and overall two‑sided alpha = 0.05.

(a) Contrast Pocock vs. O’Brien–Fleming alpha‑spending for two interim looks and report the corresponding critical z‑values at each look.

(b) Explain how mixture‑SPRT or e‑values yield always‑valid inference under arbitrary peeking (optional stopping), including the key guarantee.

(c) Describe how to integrate sequential monitoring with multiple metrics without inflating error rates.

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