The Statistical Analysis of Small Data Sets
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Product details:
- Publisher OUP Oxford
- Date of Publication 30 August 2024
- ISBN 9780198872986
- Binding Paperback
- No. of pages160 pages
- Size 234x156 mm
- Weight 278 g
- Language English 965
Categories
Short description:
This book offers advice on the statistical analysis of small data sets (which are often used for ethical, financial, or practical reasons) for various designs and levels of measurement, helping researchers to analyse such data sets, but also to evaluate and interpret others' analyses.
MoreLong description:
We live in the era of big data. However, small data sets are still common for ethical, financial, or practical reasons. Small sample sizes can cause researchers to seek out the most powerful methods to analyse their data, but they may also be wary that some methodologies and assumptions may not be appropriate when samples are small. The book offers advice on the statistical analysis of small data sets for various designs and levels of measurement, helping researchers to analyse such data sets, but also to evaluate and interpret others' analyses.
The book discusses the potential challenges associated with a small sample, as well as the ways in which these challenges can be mitigated. General topics with strong relevance to small sample sizes such as meta-analysis, sequential and adaptive designs, and multiple testing are introduced. While the focus is on hypothesis tests and confidence intervals, Bayesian analyses are also covered. Code written in the statistical software R is presented to carry out the proposed methods, many of which are not limited to use on small data sets, and the book also discusses approaches to computing the power or the necessary sample size, respectively.
Table of Contents:
General principles
Note on permutation and bootstrap tests
A single sample of continuous data
Comparing continuous data across levels of one or more factors
Correlation and regression
Binomial data
Multinomial data
Sequential analysis and adaptive designs
Meta-analysis
Multiple testing
Bayesian analysis
