This module introduces learners to inferential statistics, focusing on how to estimate population parameters, understand sampling distributions, and construct confidence intervals. It guides students through the practical application of statistical concepts to interpret real-world data and make informed decisions.

This module explores how personal bias and incomplete information impact decision-making and teaches strategies for robust estimation, critical thinking, and data analysis in uncertain conditions. Learners will gain skills in identifying and mitigating bias, understanding statistical techniques like trimming and winsorizing, and improving data reliability.

This module delves into the practical application of hypothesis testing, covering essential concepts like null hypothesis significance testing, p-values, and statistical power. Learners will explore how to interpret data, identify meaningful differences, and make informed decisions based on statistical evidence. The module also explains how sample size and confidence intervals impact the reliability of statistical conclusions.

This module explores alternative approaches to theory testing beyond traditional methods, focusing on the limitations of Null Hypothesis Significance Testing (NHST) and introducing concepts like effect sizes, confidence intervals, and Bayesian approaches. Learners will gain a deeper understanding of how scientific inquiry can be shaped by researcher intent and how to evaluate statistical methods more critically.

This module explores the role of assumptions in statistical modeling and how they influence the validity of research findings. Learners will gain an understanding of key assumptions such as independence, normality, and multicollinearity, and how to evaluate their impact on data analysis and decision-making.