Problem
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Resources
Study resources for quantitative subjects.
Explore structured guides for statistics, biostatistics, data science, programming, software, dissertation planning and quantitative research methods.
Resource library
Guides built for study, coursework and research planning.
Use these resources when you need a careful explanation of what a method does, when it is appropriate and how to interpret it.
Flagship guides
Guide 1Statistics
How to choose the correct statistical test
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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Guide 2Statistics
Understanding p-values, confidence intervals and effect sizes
A detailed guide explaining statistical significance, uncertainty, effect size, practical importance and how students should interpret results responsibly.
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Guide 3Regression
Linear regression assumptions and diagnostics
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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Guide 4Biostatistics
Survival analysis: Kaplan-Meier curves and Cox regression
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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Guide 5Software
R, Python, SPSS, SAS or Stata: which should I use?
A practical guide for students choosing statistical software for coursework, dissertations, health research, data science, biostatistics and reproducible analysis.
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Guide structure
Each guide follows a clear learning format.
Intuition
A focused section to move from the practical problem toward interpretation and responsible use.
Method
A focused section to move from the practical problem toward interpretation and responsible use.
Working
A focused section to move from the practical problem toward interpretation and responsible use.
Limitations
A focused section to move from the practical problem toward interpretation and responsible use.
Discussion
A focused section to move from the practical problem toward interpretation and responsible use.
Popular topics
regressionp-valueconfidence intervalmissing datasurvival analysislogistic regressiondissertation analysisSPSS
All resource guides
Statistics
6 guides
Foundation
How to choose the correct statistical test
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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FoundationUnderstanding p-values, confidence intervals and effect sizes
A detailed guide explaining statistical significance, uncertainty, effect size, practical importance and how students should interpret results responsibly.
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AdvancedMultiple testing and false discovery rate
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.
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IntermediateNon-parametric tests: when and how to use them
A detailed guide explaining when non-parametric tests are useful, how they differ from parametric tests, and how to interpret rank-based methods carefully.
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IntermediateANOVA, ANCOVA and comparing more than two groups
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.
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FoundationChi-square tests, Fisher's exact test and categorical data
A detailed guide to analysing categorical data, including contingency tables, chi-square tests, Fisher's exact test, expected counts, proportions and interpretation.
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Data analysis
2 guides
Foundation
How to prepare your data before analysis
A detailed guide for students learning how to clean, check, structure and document data before running statistical analysis.
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IntermediateMissing data: deletion, imputation and reporting
An advanced guide to understanding missing data mechanisms, complete-case analysis, imputation, bias, sensitivity and transparent reporting.
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Regression
4 guides
Foundation
Choosing between correlation and regression
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.
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IntermediateLinear regression assumptions and diagnostics
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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IntermediateHow to report regression results in a dissertation
An advanced guide to reporting linear, logistic and adjusted regression results clearly in dissertation chapters, including interpretation, tables, confidence intervals and limitations.
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AdvancedIntroduction to mixed-effects models
An advanced guide introducing mixed-effects models for clustered, repeated-measures and hierarchical data, including random intercepts, random slopes, interpretation and common mistakes.
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Biostatistics
8 guides
Intermediate
Logistic regression explained for health and social science students
A detailed guide to logistic regression for binary outcomes, including odds, odds ratios, interpretation, adjustment, limitations and common reporting mistakes.
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AdvancedSurvival analysis: Kaplan-Meier curves and Cox regression
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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AdvancedConfounding, mediation and effect modification
An advanced guide explaining three important ideas in observational research: confounding, mediation and effect modification, with examples, interpretation and common mistakes.
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AdvancedIntroduction to causal inference and DAGs
An advanced guide introducing causal questions, counterfactual thinking, directed acyclic graphs, confounding, colliders, mediators and why causal inference is more than regression adjustment.
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AdvancedROC curves, sensitivity, specificity and AUC
An advanced guide to diagnostic test evaluation and prediction model performance, covering sensitivity, specificity, thresholds, ROC curves, AUC and limitations.
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AdvancedLongitudinal data analysis
An advanced guide to repeated measurements over time, within-person correlation, change, trajectories, time effects, mixed models, missing follow-up and careful interpretation.
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IntermediateRisk ratios, odds ratios and rates in epidemiology
A detailed guide explaining core epidemiological effect measures, including risk, odds, rates, risk ratios, odds ratios, rate ratios and interpretation.
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AdvancedClinical trials: randomisation, blinding and intention-to-treat
A detailed guide to the core design and analysis principles of clinical trials, including randomisation, allocation concealment, blinding, intention-to-treat and bias prevention.
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Research methods
3 guides
Intermediate
Common mistakes in dissertation data analysis
An advanced guide to the most common statistical, methodological and reporting mistakes students make in dissertation data analysis, with practical ways to avoid them.
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AdvancedSample size, power and precision explained
An advanced guide explaining sample size, statistical power, precision, effect size, uncertainty and why planning should focus on estimation as well as hypothesis testing.
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AdvancedIntroduction to meta-analysis
An advanced guide to combining evidence across studies, including effect sizes, fixed-effect and random-effects models, heterogeneity, forest plots, publication bias and interpretation.
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Bioinformatics
1 guide
Software
2 guides
Advanced
Reproducible analysis with R Markdown or Quarto
An advanced guide to reproducible statistical analysis using literate programming, project structure, versioned scripts, dynamic reports, transparent decisions and reliable workflows.
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FoundationR, Python, SPSS, SAS or Stata: which should I use?
A practical guide for students choosing statistical software for coursework, dissertations, health research, data science, biostatistics and reproducible analysis.
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