---
schema: 1
type: term
title: _N-of-1_ study
description: >-
  An **_N-of-1_ study** is a study in which the evidence is gathered from a
  single individual rather than a population, reflecting the channel's
  discussion of AI's move in science and medicine toward **individual,
  contextual predictions** instead of broad, population-level theories.
canonical: 'https://liquidgold.cc/channel/everyinc/explore/term/n-of-1-study-1507cc603b0f'
markdown: >-
  https://liquidgold.cc/channel/everyinc/explore/term/n-of-1-study-1507cc603b0f.md
llms: null
parent: 'https://liquidgold.cc/channel/everyinc/llms.txt'
channel: everyinc
updated: '2026-10-23T22:12:35.346Z'
language: null
children: []
---


# \_N-of-1\_ study

Canonical: https://liquidgold.cc/channel/everyinc/explore/term/n-of-1-study-1507cc603b0f

Parent: [Channel index](https://liquidgold.cc/channel/everyinc/llms.txt)

## Definition

An **_N-of-1_ study** is a study in which the evidence is gathered from a single individual rather than a population, reflecting the channel's discussion of AI's move in science and medicine toward **individual, contextual predictions** instead of broad, population-level theories.

In this channel's material, the **_N-of-1_ study** appears in the context of AI's shift in science and medicine: away from broad theories that describe populations, and toward **individual, contextual predictions** tailored to a single person or case. An N-of-1 study is the extreme end of that shift — a study where the *sample is one individual*, and the evidence is built around that person's specific context rather than averaged across a group.The discussion of **data-first science** helps explain the appeal: when the underlying data is published alongside conclusions — including failures and edge cases — it becomes possible to build understanding that is contextual and individual rather than only general. This is part of how AI is reshaping what scientific evidence can look like in medicine.- An **_N-of-1_ study** is a study built around a single individual as the unit of evidence. - It connects to the channel's theme of moving from broad, population-level theories toward **individual, contextual predictions**. - Data transparency — sharing underlying data, not just successful outcomes — is what makes such individualized evidence usable.