Quantitative vs Qualitative Research

Quantitative research measures and counts to test hypotheses and generalise — it deals in numbers, statistics, p-values and effect sizes. Qualitative research explores meaning, experience and behaviour through interviews, focus groups and observation, analysed thematically. Its rigour is judged by trustworthiness, not sample size.

The evidence pyramid orders quantitative designs by risk of bias. Qualitative research is not a weak rung at the bottom — it answers a different question (“why/how”, not “how much”), so it is appraised on its own terms rather than by where it would land on this ladder.

Use the pyramid for quantitative “how much / does it work” questions. For “why do patients leave the ED before being seen?” or “how do staff experience handover?”, reach instead for the qualitative checklist: was the method appropriate, the sampling purposive, the analysis transparent, and reflexivity addressed?

Marking a qualitative paper down for being “too small” or “having no statistics” is a category error — like criticising a thermometer for not weighing things. Each paradigm has its own yardstick, and the right one depends on the question being asked.

• Quantitative → numbers, hypothesis-testing, generalisability
• Qualitative → meaning, experience, the “why/how”
• Qualitative rigour = trustworthiness, not p-values

A qualitative study of the patient experience of waiting in a busy ED. Researchers conduct semi-structured interviews with a purposive sample of patients and staff until no new ideas emerge (data saturation), then analyse transcripts thematically — surfacing themes such as feeling forgotten, uncertainty about timing, and the value of brief updates. There is no p-value and no claim of statistical generalisability; the findings transfer where the context is similar. Rigour here is judged by trustworthiness — credibility, transferability, dependability and confirmability — not by sample size or significance testing. A small, well-conducted qualitative study is not “underpowered”.

• Demanding statistical generalisability from qualitative work — it offers transferability, not generalisability.
• The “small sample” criticism — qualitative sample size is driven by saturation and richness, not power calculations.
• Ignoring reflexivity — the researcher’s influence on data and analysis must be acknowledged, not treated as a flaw to score down.