Study guides
Learn the core ideas behind statistics, biostatistics and research methods.
These guides explain important quantitative concepts such as p-values, confidence intervals, regression, probability, study design, medical statistics and data interpretation. Each guide is written to help students understand the idea, avoid common mistakes and connect the method to real academic or health data problems.
Resources
Focused guides for quantitative study.
Read clear guides on statistical methods, interpretation, research planning and applied data analysis.
Guide areas
Start with these
Essential study guides.
These guides cover the ideas students commonly need when learning statistics and research methods.
Statistics · Foundation
How to choose the correct statistical test
6 min read · Updated 5 June 2026
A detailed guide for students deciding between t-tests, ANOVA, chi-square tests, correlation, regression, logistic regression and non-parametric methods.
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Statistics · Foundation
Understanding p-values, confidence intervals and effect sizes
6 min read · Updated 5 June 2026
A detailed guide explaining statistical significance, uncertainty, effect size, practical importance and how students should interpret results responsibly.
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Regression · Intermediate
Linear regression assumptions and diagnostics
5 min read · Updated 5 June 2026
A detailed guide to the assumptions behind linear regression, why they matter, how students should think about diagnostics and how to report limitations clearly.
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Biostatistics · Advanced
Survival analysis: Kaplan-Meier curves and Cox regression
5 min read · Updated 5 June 2026
An advanced guide to time-to-event data, censoring, Kaplan-Meier curves, log-rank tests, Cox regression, hazard ratios and careful interpretation in medical research.
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All guides
Statistics
How to choose the correct statistical test
6 min read · Updated 5 June 2026
A detailed guide for students deciding between t-tests, ANOVA, chi-square tests, correlation, regression, logistic regression and non-parametric methods.
Data analysis
How to prepare your data before analysis
5 min read · Updated 5 June 2026
A detailed guide for students learning how to clean, check, structure and document data before running statistical analysis.
Statistics
Understanding p-values, confidence intervals and effect sizes
6 min read · Updated 5 June 2026
A detailed guide explaining statistical significance, uncertainty, effect size, practical importance and how students should interpret results responsibly.
Regression
Choosing between correlation and regression
6 min read · Updated 5 June 2026
A detailed guide helping students understand when to use correlation, when to use regression, and why the research question matters more than the software menu.
Regression
Linear regression assumptions and diagnostics
5 min read · Updated 5 June 2026
A detailed guide to the assumptions behind linear regression, why they matter, how students should think about diagnostics and how to report limitations clearly.
Biostatistics
Logistic regression explained for health and social science students
6 min read · Updated 5 June 2026
A detailed guide to logistic regression for binary outcomes, including odds, odds ratios, interpretation, adjustment, limitations and common reporting mistakes.
Research methods
Common mistakes in dissertation data analysis
5 min read · Updated 5 June 2026
An advanced guide to the most common statistical, methodological and reporting mistakes students make in dissertation data analysis, with practical ways to avoid them.
Regression
How to report regression results in a dissertation
5 min read · Updated 5 June 2026
An advanced guide to reporting linear, logistic and adjusted regression results clearly in dissertation chapters, including interpretation, tables, confidence intervals and limitations.
Data analysis
Missing data: deletion, imputation and reporting
6 min read · Updated 5 June 2026
An advanced guide to understanding missing data mechanisms, complete-case analysis, imputation, bias, sensitivity and transparent reporting.
Research methods
Sample size, power and precision explained
6 min read · Updated 5 June 2026
An advanced guide explaining sample size, statistical power, precision, effect size, uncertainty and why planning should focus on estimation as well as hypothesis testing.
Biostatistics
Survival analysis: Kaplan-Meier curves and Cox regression
5 min read · Updated 5 June 2026
An advanced guide to time-to-event data, censoring, Kaplan-Meier curves, log-rank tests, Cox regression, hazard ratios and careful interpretation in medical research.
Biostatistics
Confounding, mediation and effect modification
5 min read · Updated 5 June 2026
An advanced guide explaining three important ideas in observational research: confounding, mediation and effect modification, with examples, interpretation and common mistakes.
Biostatistics
Introduction to causal inference and DAGs
6 min read · Updated 5 June 2026
An advanced guide introducing causal questions, counterfactual thinking, directed acyclic graphs, confounding, colliders, mediators and why causal inference is more than regression adjustment.
Biostatistics
ROC curves, sensitivity, specificity and AUC
5 min read · Updated 5 June 2026
An advanced guide to diagnostic test evaluation and prediction model performance, covering sensitivity, specificity, thresholds, ROC curves, AUC and limitations.
Statistics
Multiple testing and false discovery rate
5 min read · Updated 5 June 2026
An advanced guide explaining why repeated hypothesis testing increases false positives, how family-wise error and false discovery rate differ, and how to report multiple-testing corrections.
Regression
Introduction to mixed-effects models
5 min read · Updated 5 June 2026
An advanced guide introducing mixed-effects models for clustered, repeated-measures and hierarchical data, including random intercepts, random slopes, interpretation and common mistakes.
Biostatistics
Longitudinal data analysis
6 min read · Updated 5 June 2026
An advanced guide to repeated measurements over time, within-person correlation, change, trajectories, time effects, mixed models, missing follow-up and careful interpretation.
Research methods
Introduction to meta-analysis
5 min read · Updated 5 June 2026
An advanced guide to combining evidence across studies, including effect sizes, fixed-effect and random-effects models, heterogeneity, forest plots, publication bias and interpretation.
Bioinformatics
RNA-seq and differential expression analysis
5 min read · Updated 5 June 2026
An advanced guide introducing RNA-seq differential expression analysis, count data, quality control, normalisation, experimental design, multiple testing and biological interpretation.
Software
Reproducible analysis with R Markdown or Quarto
5 min read · Updated 5 June 2026
An advanced guide to reproducible statistical analysis using literate programming, project structure, versioned scripts, dynamic reports, transparent decisions and reliable workflows.
Statistics
Non-parametric tests: when and how to use them
5 min read · Updated 5 June 2026
A detailed guide explaining when non-parametric tests are useful, how they differ from parametric tests, and how to interpret rank-based methods carefully.
Statistics
ANOVA, ANCOVA and comparing more than two groups
5 min read · Updated 5 June 2026
A detailed guide explaining how to compare more than two groups using ANOVA, when ANCOVA is useful, how post-hoc tests work, and how to avoid multiple-testing mistakes.
Statistics
Chi-square tests, Fisher's exact test and categorical data
5 min read · Updated 5 June 2026
A detailed guide to analysing categorical data, including contingency tables, chi-square tests, Fisher's exact test, expected counts, proportions and interpretation.
Biostatistics
Risk ratios, odds ratios and rates in epidemiology
5 min read · Updated 5 June 2026
A detailed guide explaining core epidemiological effect measures, including risk, odds, rates, risk ratios, odds ratios, rate ratios and interpretation.
Biostatistics
Clinical trials: randomisation, blinding and intention-to-treat
5 min read · Updated 5 June 2026
A detailed guide to the core design and analysis principles of clinical trials, including randomisation, allocation concealment, blinding, intention-to-treat and bias prevention.
Software
R, Python, SPSS, SAS or Stata: which should I use?
7 min read · Updated 5 June 2026
A practical guide for students choosing statistical software for coursework, dissertations, health research, data science, biostatistics and reproducible analysis.