Statistics for psychology students, from confusion to confidence

What Is Data?

4 lessons · NOIR scales, continuous vs discrete, the °C trap

Foundations · Research Design

IV vs DV, between vs within subjects, the Design Trap

Foundations · Describing Data & Cronbach's Alpha

Descriptives by scale, skewness & kurtosis, scale reliability

Foundations · Hypothesis Testing & p-values

H₀ and H₁, what p = .032 actually means, the four things p is not

Foundations · Effect Sizes

Cohen's d, Pearson r, eta-squared, significant ≠ important

Foundations · Normality

Shapiro-Wilk, Q-Q plots, the parametric/non-parametric gateway

Two Independent Groups

Welch's t-test + Mann-Whitney U, with Jamovi screencast

Same Group Twice

Paired t-test + Wilcoxon Signed-Rank

Three or More Independent Groups

One-way ANOVA + Kruskal-Wallis, post-hocs

Same Group Three or More Times

Repeated Measures ANOVA + Friedman, sphericity

Correlation

Pearson r + Spearman rho, the causation section

Simple Linear Regression

B, beta, R², and the residuals point

Chi-Square & Fisher's Exact

Categorical × categorical, expected cell counts

How to Choose the Right Test

The six-question decision framework + trap scenarios

Viva Mastery

The Ten Questions, the Justification Script, mock viva