# Network Dependence Can Lead to Spurious Associations and Invalid Inference

## TL;DR

This paper highlights how social network dependence can cause false associations and unreliable statistical conclusions in health and social science research, especially when ignoring network ties in data analysis.

## Contribution

It demonstrates that network dependence can lead to spurious findings and proposes a test adapted from spatial autocorrelation to detect such dependence in existing studies.

## Key findings

- Network dependence can cause spurious associations in research.
- Many influential studies may have invalid conclusions due to unacknowledged network ties.
- A statistical test for network dependence reveals its presence in key health studies.

## Abstract

Researchers across the health and social sciences generally assume that observations are independent, even while relying on convenience samples that draw subjects from one or a small number of communities, schools, hospitals, etc. A paradigmatic example of this is the Framingham Heart Study (FHS). Many of the limitations of such samples are well-known, but the issue of statistical dependence due to social network ties has not previously been addressed. We show that, along with anticonservative variance estimation, this can result in spurious associations due to network dependence. Using a statistical test that we adapted from one developed for spatial autocorrelation, we test for network dependence in several of the thousands of influential papers that have been published using FHS data. Results suggest that some of the many decades of research on coronary heart disease, other health outcomes, and peer influence using FHS data may suffer from spurious associations, error-prone point estimates, and anticonservative inference due to unacknowledged network dependence. These issues are not unique to the FHS; as researchers in psychology, medicine, and beyond grapple with replication failures, this unacknowledged source of invalid statistical inference should be part of the conversation.