---

---

Introduction

Rigour gets talked about a lot in applied research, but it’s used inconsistently and rarely defined with any precision.

Sometimes it shows up as aspiration: “We need more rigour, something closer to academic standards.” Other times as critique: “That wasn’t rigorous.” In both cases, it tends to function as shorthand for better, without being explicit about what better actually means in context.

When the meaning isn’t clear, teams tend to default to what’s visible. Larger samples, more studies, more complex analysis, heavier process. The work expands, but it’s not always obvious that the quality of insight improves with it. You can end up with something that looks like rigour from the outside, without it necessarily changing the decision being made.

Used this way, rigour can generate more activity, without much additional clarity about what that activity is for.

Negotiating rigour’s meaning

In practice, “more rigour” rarely means one thing.

When stakeholders ask for it, they’re often reacting to a mix of underlying concerns. For example:

• Confidence: Can I trust this enough to act on it?
• Credibility: Will this stand up to scrutiny?
• Traceability: Can I see how you got there?
• Appropriateness: Was this the right approach for the question?
• Reproducibility (sometimes): Would we see the same again?

None of these are method-specific, but they show up differently depending on the approach being used.

In quantitative work, confidence isn’t just a function of sample size. It’s whether the data actually bears on the decision in question. A large sample answering the wrong question doesn’t resolve much.

In qualitative work, credibility comes less from the volume of documentation and more from how well the insight is grounded. It’s whether the data has been triangulated, whether alternative interpretations have been considered, and how well the conclusions are rooted in the data.

Seen this way, rigour doesn’t behave like a universal bar you either clear or don’t. It’s a set of expectations and those expectations need to be made explicit before they can be meaningfully met.

Is trustworthiness enough?

In qualitative research, rigour is often framed through the trustworthiness criteria introduced by Lincoln & Guba: credibility, dependability, confirmability, and transferability.

These are useful because they make parts of the problem more concrete. They shift the conversation away from vague signals of quality toward properties you can actually examine in the work.

But they don’t quite cover the whole question.

Trustworthiness is largely about the integrity of the finding: are they well-grounded, traceable, and supported by the data? Rigour, at least in practice, tends to extend beyond that. It includes whether the approach itself is appropriate for the decision at hand, and whether the output is sufficient to support that decision with confidence.

The difference matters in practice.

You can have a carefully conducted qualitative study (transparent process, well-structured analysis), that feels internally sound, but still isn’t enough to support a decisoin that ultimately depends on quantification (for example, sizing an opportunity or validating a trade-off at scale).

Equally, you can have work that appears rigorous on the surface (detailed process, extensive documentation), but where the underlying insight doesn’t hold up particularly well.

How rigour gets misapplied

The ambiguity of rigour can create predictable patterns and collapse into proxies.

In quantitative research, that often means statistical signals: larger samples, lower p-values. These become stand-ins for rigour, even when they’re only loosely connected to the decision being made.

In qualitative research, it becomes more procedural: more frameworks, more artefacts, more visible process. The emphasis shifts towards how the work is presented, rather than what it actually supports.

In both cases, rigour becomes something to demonstrate. Whether it improves the quality of the decision is a separate question.

Responding to “more rigour”

If rigour is acting as a proxy for multiple concerns, the most useful move is usually to slow down and make those concerns explicit. Ask what specifically is worrying them. Ask what decision this needs to support, and what would shift their confidence. These questions shift the question from a general critique to something more concrete. Often, the concern turns out to be more narrower and more addressable than “more rigour” initially suggests.

In conclusion

Rigour doesn’t map neatly to a method, a sample size, or a fixed standard.

In practice, it’s closer to the degree to which a piece of research is fit for purpose: grounded in an appropriate approach, transparent in how it is constructed, and sufficient for the decision its intended to inform.

The work then, is less about defending rigour after the fact, and more about negotiating the expectations of rigour explict and early on so that what “good enough” looks like is understood before the research begins.