Statistics

  1. Statistics in Medicine

    A practical guide to statistics in medicine: study design, descriptive and inferential methods, and how to read clinical results correctly.

  2. Data Type

    A comprehensive guide to understanding statistical data types in statistics, exploring the differences between discrete, continuous, categorical, and ordinal variables, including derived data. An essential article for those approaching statistical analysis

  3. Statistical Distributions

    Statistical distributions explained: normal, binomial, Poisson and t, when each applies, and how to visualise them in Python.

  4. Central Limit Theorem

    Why sample means converge to a normal distribution regardless of population shape: the Central Limit Theorem explained with conditions and medical applications.

  5. Descriptive Statistics

    A complete guide to descriptive statistics: central tendency (mean, median, mode), variability (variance, SD), shape (skewness, kurtosis), and quartiles explained.

  6. Study Design in Medicine

    Learn the main types of clinical study designs – cohort, case-control, cross-sectional, and RCT – with practical examples for doctors and researchers.

  7. Inferential Statistics: Hypothesis Testing

    Practical guide to hypothesis testing: formulate H0 and H1, interpret p-values, understand Type I/II errors, and calculate statistical power for clinical research.

  8. Choose the Right Statistical Test

    Choose the right statistical test by variable type, distribution, sample size, design, and objective, with clear parametric vs non‑parametric guidance.

  9. T test

    Concise guide to t-test: single-sample, independent, and paired; assumptions, Levene and Welch; with practical Python scipy.stats examples.

  10. One-way ANOVA

    One-way ANOVA explained: when to use it, assumptions, F‑statistic, post‑hoc tests, contrasts, and Python examples to compare means across 3+ groups.

  11. Repeated measure ANOVA

    Repeated measures ANOVA explained: when to use it, key assumptions (normality, sphericity), Mauchly and corrections, multivariate options, and practical examples.

  12. Correlations

    Explore key statistical concepts including correlation, Spearman's Rank Order Correlation, and Kendall coefficients in this comprehensive lesson. Learn about their applications in data analysis, with practical examples and Python code demonstrations.

  13. Linear Regression

    Comprehensive guide to linear regression, covering its principles, assumptions, and applications in medical research. Includes explanations of simple and multiple regression, key statistical concepts, and alternative models..

  14. Logistic Regression 1

    Introduction to logistic regression for clinicians: odds ratios, model assumptions and interpretation, with worked Python examples.

  15. Logistic Regression 2

    Advanced logistic regression: model fit with -2LL and Hosmer-Lemeshow, ROC/AUC, discrimination and calibration, with Python plots.

  16. Chi Square

    Explore the chi-square test, a powerful statistical tool for analyzing categorical data. Learn about its applications, limitations, and implementation in Python, including data characteristics, hypothesis testing, and practical examples

  17. ANCOVA

    Comprehensive guide to ANCOVA (Analysis of Covariance) in statistical analysis. Covers definition, assumptions, Python implementation, and interpretation of results using a hypertension treatment case study.

  18. Bayes’ Theorem

    Bayes' theorem for clinicians: prior, likelihood and posterior probability, and why test accuracy depends on disease prevalence.

  19. Monte Carlo Simulation

    A detailed exploration of Monte Carlo simulation methods in medical applications, covering statistical foundations, implementation in Python, practical examples in survival analysis and risk assessment, limitations, and benefits. Includes code examples for healthcare professionals and researchers.

  20. Nonparametric statistics

    A guide to nonparametric statistics covering ranks, ties, counts, and proportions. Includes Python code for major tests like Mann-Whitney U, Wilcoxon, Kruskal-Wallis, Spearman's correlation, Friedman, and Chi-square, with focus on medical research applications and their advantages and limitations.

  21. Survival Analysis

    Clear, practical guide to survival analysis: KM curves, hazards, Cox models, assumptions, Python lifelines, and diagnostics for clinical data.

  22. Propensity Score

    Clear and practical introduction to the Propensity Score in medical research. What it is, how it works, when to use it, strengths and limitations, and a full step-by-step Python example using simulated clinical data.