Learn statistics the way researchers use it

Types of Data: Scales of Measurement

Learn the four scales of measurement — nominal, ordinal, interval, and ratio — and why they determine which statistical test you can use.

Descriptive Statistics: Summarizing Your Data

Understand mean, median, mode, standard deviation, and how to describe the shape of your data distribution before running any inferential test.

The Normal Distribution Explained

Understand why the bell curve is central to statistics, what makes data "normal," and how to check your data for normality before running parametric tests.

How to Read Statistical Results

Learn to interpret p-values, confidence intervals, effect sizes, and the standard tables found in research papers and statistical software output.

Visualizing Data: Choosing the Right Chart

Learn which chart types suit different data structures — from histograms to scatter plots — and why a good visualization tells a story that numbers alone cannot.

Confidence Intervals: What They Really Mean

Learn what a 95% confidence interval actually tells you, how CI width reflects precision, and why CIs convey more information than p-values alone.

Missing Data: Types, Risks, and Solutions

Understand MCAR, MAR, and MNAR — the three types of missing data — and which methods for handling them are valid versus dangerously misleading.

Effect Size: Why Statistical Significance Is Not Enough

Understand Cohen's d, odds ratios, and other effect size measures — and why a statistically significant result can still be clinically meaningless.

Introduction to Hypothesis Testing

Understand null and alternative hypotheses, Type I and Type II errors, the logic of p-values, and the difference between statistical and practical significance.

How to Choose the Right Statistical Test

A step-by-step decision guide for selecting the correct statistical test based on your research question, data types, number of groups, and assumptions.

The Normal Distribution and Z-Scores

Understand why the bell curve appears everywhere in statistics, how to use the 68-95-99.7 rule, and how Z-scores let you compare observations on any scale.

Statistical Power and Sample Size

Understand the relationship between α, β, power, and effect size through interactive visualizations. Learn why underpowered studies fail to find real effects.

T-Tests: Comparing Means Between Groups

Master the one-sample, independent samples, and paired t-tests: when to use each, what assumptions to check, and how to interpret the output.

ANOVA: Comparing Means Across Multiple Groups

Learn when to use one-way ANOVA instead of multiple t-tests, how to interpret the F-statistic and ANOVA table, and why post-hoc tests are needed.

Chi-Square Test of Independence

Test whether two categorical variables are associated using the chi-square test — from constructing contingency tables to interpreting Cramér's V effect size.

Correlation Analysis

Measure and interpret linear relationships between continuous variables using Pearson and Spearman correlations, and understand why correlation does not equal causation.

Nonparametric Tests

When parametric assumptions are violated, nonparametric tests provide a distribution-free alternative. Learn Mann-Whitney U, Wilcoxon, and Kruskal-Wallis.

Linear Regression

Understand simple and multiple linear regression: model building, assumption checking, coefficient interpretation, and R² — the workhorse of quantitative research.

Logistic Regression

Predict binary outcomes and estimate odds ratios using logistic regression — the standard method for clinical prediction models and risk factor analysis.

Scatter Plots and Linear Regression

Explore how scatter plots reveal relationships between variables, how to fit a regression line, and how to interpret the slope, intercept, and R² in plain language.

Factor Analysis

Discover the latent constructs underlying your questionnaire data using exploratory and confirmatory factor analysis — essential for validating psychological scales.

Cox Regression: Survival Analysis with Covariates

Learn how the Cox proportional hazards model extends Kaplan-Meier analysis to multiple covariates, how to interpret hazard ratios, and how to check the proportional hazards assumption.

Survival Analysis

Analyze time-to-event data using Kaplan-Meier curves, the log-rank test, and Cox proportional hazards regression — essential methods for clinical and epidemiological research.

Sample Size and Statistical Power

Learn why underpowered studies waste resources and mislead, how to calculate the sample size you need for any study design, and what effect size to anticipate.

ROC Curves and Diagnostic Accuracy

Evaluate the diagnostic performance of a test or prediction model using ROC curves, AUC, sensitivity, specificity, and optimal cutoff selection.

Reading Kaplan-Meier Survival Curves

Learn to read, interpret, and critically evaluate Kaplan-Meier curves — the standard visualization for time-to-event data in clinical research.

Sensitivity, Specificity, PPV, and NPV

Master the 2×2 table for diagnostic tests — and understand why positive predictive value changes dramatically with disease prevalence.

Table 1: Baseline Characteristics

Learn how to build and present the baseline characteristics table — the first table in nearly every clinical paper — correctly and completely.

STROBE: Reporting Observational Studies

Learn how to apply the STROBE checklist to ensure your cohort, case-control, or cross-sectional study is reported completely and transparently.

CONSORT: Reporting Randomized Trials

Understand the 25-item CONSORT checklist and the mandatory flow diagram for transparent reporting of randomized controlled trials.

PRISMA: Reporting Systematic Reviews

Learn the PRISMA 2020 checklist and flow diagram for systematic reviews and meta-analyses — the most cited reporting guideline in biomedical research.

How to Write a Complete Methods Section

A practical guide to writing the statistical methods section of a manuscript — covering every element peer reviewers and reporting guidelines require.

Machine Learning for Researchers: A Practical Introduction

Understand the ML workflow — training, validation, testing — and how it differs from traditional statistical hypothesis testing. No coding required.

SHAP Values: Explaining ML Model Predictions

Understand how SHAP (SHapley Additive exPlanations) values measure each feature's contribution to individual predictions — and how to present them to clinical audiences.

Cross-Validation: A More Honest Model Evaluation

Learn why a single train/test split is not enough and how k-fold cross-validation gives a more reliable estimate of how your model will perform in the real world.

Preparing Time Variables for Trend Analysis

Learn how to format, validate, and aggregate date and time variables correctly — the essential first step before any time series or trend analysis.

Interpreting Trends: Real Change vs Random Variation

Learn how to distinguish genuine trends from random fluctuation in longitudinal data, and how to describe trends correctly in your results section.