This module introduces the concepts of descriptive and inferential uncertainty in psychological research, guiding learners through probability-based inference and its application to both individual and group data. Learners will gain practical tools for evaluating research findings and making confident, evidence-based conclusions.

This module introduces the t-test as a statistical tool for comparing means when population parameters are unknown. Learners will explore different types of t-tests, interpret results using probability and confidence intervals, and understand the assumptions and controversies surrounding hypothesis testing. Practical examples and alternative approaches to inference are also discussed.

This module introduces the concept of correlation, guiding learners through the process of quantifying and interpreting relationships between variables using Pearson's r. Participants will learn to visualize bivariate data, calculate correlation coefficients, and recognize the limitations and assumptions of correlational analysis. Practical examples and cautions help ensure accurate interpretation and application of results.

This module introduces the principles and applications of analysis of variance (ANOVA) for comparing means across multiple groups. Learners will explore the rationale for analyzing variances, understand the calculation and interpretation of F-ratios, and examine significance testing and effect sizes in research contexts. The module also covers both one-way and two-way ANOVA, as well as practical considerations for conducting and evaluating ANOVA in experimental studies.

This module introduces statistical methods for analyzing categorical and non-normally distributed data, including chi-square and distribution-free tests. Learners will discover how to select and apply appropriate tests based on data characteristics and research questions. Practical examples and checklists help reinforce the decision-making process for real-world data analysis.