---
id: "prereq-qual-quant-tradeoff"
type: "prereq"
source_timestamps: ["¶ 2"]
tags: ["market-research-fundamentals"]
related: ["claim-ai-resolves-research-tradeoff", "concept-llm-based-interviewers"]
reason: "Necessary to appreciate the magnitude of the disruption caused by AI moderators, which claim to resolve this fundamental industry constraint."
sources: ["commercial"]
sourceVaultSlug: "hbr-seg-commercial"
originDay: 5
articleStem: "hbr-new-30-ai-scale-customer-research"
sourceUrl: "https://hbr.org/2026/04/how-ai-helps-scale-qualitative-customer-research"
sourceTitle: "How AI Helps Scale Qualitative Customer Research"
---
# Understanding of the Qual vs. Quant Tradeoff

**Prerequisite.** The article assumes the reader understands the historical limitations of market research: **quantitative** methods offer statistical power but lack depth; **qualitative** methods offer rich insight but lack generalizability and scale. Historically you had to choose.

**Why it matters.** Without this, a reader cannot appreciate the magnitude of the disruption AI moderators claim — resolving (or, more precisely, *narrowing*) this fundamental constraint. It directly underpins [[claim-ai-resolves-research-tradeoff]] and the value of [[concept-llm-based-interviewers]].

**Enrichment framing.** A domain expert would place AI-moderated qual inside **mixed-methods** theory (Creswell & Plano Clark): it fits naturally into *exploratory* or *explanatory sequential* designs, where AI-generated themes inform or explain survey results — i.e., AI adds a **new tier** to the qual/quant stack rather than collapsing the two.