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Quantitative research
Research that uses numerical data and statistical analysis to measure patterns, differences or relationships.
Why it matters
Some decisions depend on how often something happens, how much people spend or whether a measured difference is meaningful. Quantitative research gives you a structured way to investigate those questions using numerical evidence.
The usefulness of the result depends on the measurement and the people or events included. Define the question, population, time period and method before collecting data. A precise-looking percentage cannot repair a biased question or a sample that misses important groups.
A simple example
Illustrative example, not client research
Suppose a training provider asks recent course enquirers who did not book to select their main reason, and receives 200 usable responses.
Describe the result accurately
If 60 respondents select the available session times, that is 30% of the 200 respondents.
Report the question, response options, collection period and number of responses alongside the percentage.
Check who the result represents
People who replied may differ from those who did not. Recent enquirers may also differ from the wider audience the provider hopes to reach.
Review recruitment and non-response before generalizing. Do not describe 30% of respondents as 30% of the whole market without an appropriate basis.
The figures are invented for illustration. They do not establish a population estimate, explain every person’s reason or prove that adding new session times would increase bookings.
A relationship does not prove a cause
Two measures can change together without one causing the other. A rise in bookings after a price change might also reflect seasonality, a new campaign or a change in the audience.
Choose a study design that fits the claim you need to make. Report uncertainty where appropriate, and distinguish a statistically detectable difference from one large enough to matter to the business. Qualitative work can help explore the context behind a pattern.
Before acting on a number
- What exactly was measured, and who or what is included in the denominator?
- Could recruitment, missing responses or question wording distort the result?
- Does the evidence support a description, an estimate or a causal claim, and how much uncertainty remains?