Quantitative Analysis with Small Samples: A Practical Guide for Students and Early-Career Researchers
Small Sample Lab
A published open textbook on rigorous quantitative analysis when sample sizes are small. Written for students, early-career researchers, and analysts across education, health, business, and resource-constrained settings. Free to read online under CC BY 4.0.
Publication Details
Published: 16 May 2026
Version: v0.1.0
Zenodo DOI: 10.5281/zenodo.20221929
Licence: CC BY 4.0
18 chapters · 5 worked R projects
Positioning
In a big-data and machine-learning era, many real studies still operate with small samples, limited recruitment, pilot data, or constrained field settings. That is exactly where bad method choices become expensive. Small Sample Lab is positioned for readers who need methods that are careful, teachable, and realistic when n is modest by necessity.
Who It Is For
Small Sample Lab is being developed for:
- students, taught masters learners, and early-career PhD researchers
- users in education, health, business, and social-science settings
- analysts and practitioners working with samples of roughly
10to100cases - readers who need method guidance that is statistically careful and operationally realistic
What The Core Project Already Contains
Generated Datasets
The source project already includes scripts that generate working datasets for realistic examples, so the line is not starting from a blank outline.
Helper Functions
Reusable R helpers already exist for exact tests, bootstrap intervals, Wilcoxon procedures, and Firth logistic regression.
Guided Labs
The planned lab sequence already covers Fisher's exact test, bootstrap confidence intervals, rank tests, reliability analysis, power planning, imputation, and data screening.
Worked Projects
The source outline already includes integrated projects such as campaign evaluation, short-scale reliability assessment, and paired intervention evaluation.
Beyond The Book
Small Sample Lab is being developed as more than a single manuscript. The underlying project already supports a broader methods line built around guided application, teaching use, and careful workflow support for limited-data studies.
Guided Labs
Step-by-step practicals help readers move from explanation to implementation rather than stopping at theory.
Applied Examples
Worked projects and synthetic datasets give the methods line concrete business, education, and research settings.
Teaching Resources
The project design already anticipates lab practicals, instructor-facing support, and teaching-oriented companion material.
Methods Review Support
Small-sample workflows also connect naturally to project-specific review and interpretation support where limited data make method choice critical.
What The Line Covers
The methods range already anchored in the source project includes:
- exact tests and resampling methods
- nonparametric rank-based procedures
- penalized and Bayesian regression for limited data
- reliability and measurement-quality checks for short scales
- multi-criteria decision-making in small-case settings
- effect sizes, confidence intervals, and transparent interpretation
This range matters because small studies should not be treated as a compromise case by default. With the right methods and clear reporting, they can still be analysed carefully and interpreted honestly.
Cite This Book
Sharafuddin, M. A., Jaleel, A. A., & Madhavan, M. (2026). Quantitative Analysis with Small Samples: A Practical Guide for Students and Early-Career Researchers (v0.1.0). Zenodo. https://doi.org/10.5281/zenodo.20221929
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Companion Resources
Lab practicals, instructor manual, Moodle pack, and slide decks available separately.
Contact for access →