---
title: Methodological Pluralism
type: concept
tags: [philosophy-of-science, methodology, feyerabend, longino, pluralism]
sources: ["[[sources/2023-anjum-rocca-phi403-causation-in-science]]", "[[sources/2023-anjum-rocca-phi403-lecture-20-plural-methods-one-causation]]"]
created: 2026-04-20
updated: 2026-04-20
---

# Methodological Pluralism

The view that **no single scientific method suffices** to establish scientific knowledge; several, potentially incommensurable, methods are needed. Originally J.S. Mill; systematised by **Paul Feyerabend** (*Against Method*) and **Helen Longino**.

## Two readings
- **Descriptive** — as a matter of fact, science does use plural methods.
- **Normative** — science *should* use plural methods to avoid losing out on knowledge. This is the version the course defends.

Longino: any single approach to science is incomplete; there can be more than one correct scientific account of the world.

## The course's application

Anjum & Rocca argue in [[sources/2023-anjum-rocca-phi403-lecture-20-plural-methods-one-causation]] that methodological pluralism follows from the nature of [[concepts/causation]]:

- Causation has **many symptoms** — difference-making, regularity, manipulability, probability-raising, energy transference.
- No symptom accompanies causation in every case.
- Therefore each symptom requires its own method, and no single method is reliable for all cases.

This can be combined with **causal monism** (one causation, many symptoms) or **causal pluralism** (many causations). See [[concepts/causal-pluralism]].

## Evidence ranking with conflicting methods
When methods disagree, methodological pluralism requires an **evidence-ranking scheme** — not the rigid [[concepts/evidence-hierarchy|EBM hierarchy]] that automatically privileges RCTs, but a context-sensitive weighing of which method's symptom is most reliable for the case at hand.

## Implications for process / data science
- Don't rely solely on statistical benchmarks ([[concepts/rct-limitations]]).
- Combine mechanistic / causal reasoning ([[concepts/causal-process-discovery]]) with correlational methods ([[concepts/predictive-process-monitoring|PPM]]).
- Qualitative case studies, interviews, and small-N work answer *different* causal questions than large-N statistics.

## Related
[[concepts/causal-pluralism]] · [[concepts/evidence-hierarchy]] · [[concepts/causation]] · [[concepts/rct-limitations]]