---
title: Sampling and sample frame
description: "We have tried to be careful in the book to use \"**dataset**\" instead of \"**sample**\" when talking about our example data as we think this helps remind us that both statistical paradigms of **hypothesis testing** and of **confidence intervals** assume…"
url: "https://www.psyctc.org/psyctc/glossary2/dataset/"
updated: "2021-08-07"
category: All OM book glossary entries
---

# Sampling and sample frame

We have tried to be careful in the book to use "**dataset**" instead of "**sample**" when talking about our example data as we think this helps remind us that both statistical paradigms of **hypothesis testing** and of **confidence intervals** assume random sampling and are each brilliant for their purposes where that applied but that using them for data that may be nearer a census than a traditional sample, and which is never really a random sample, carries risks of bias and limits on generalisability.

Where data really are sampled from a population the sample frame tells us how the sample was created. For example, if to make things manageable only a random subset of clients' data are analysed, then the sample frame might be "a 10% sample of the entire set of clients' data were selected using a set of random numbers". In research it might be that referential data was developed by, e.g. "contacting individuals selected at random from the electoral register". The first is probably a genuinely unbiased sampling frame, the second, while good, won't be: not everyone is on the electoral register and almost certainly a non-random subset of people contacted will refused the survey.

#### Try also ...

Sample
Population
Estimation
Confidence intervals
Generalisability

#### Chapters

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