Sampling Techniques, Distribution, CLT, Hypothesis Testing Basics, Z-Test, T-Test, ANOVA, Chi-Square, Regression Analysis
Sampling Techniques, Distribution, CLT, Hypothesis Testing Basics, Z-Test , T-Test , ANOVA , Chi-Square , Regression Analysis , etc. (Quantitative Reasoning Course for BS/ B.Ed Hons Level) The fundamental concepts of inferential statistics form a logical progression: we begin by selecting a representative sample , describe its distribution , use the Central Limit Theorem to justify normal-based methods, frame hypotheses, and finally apply the appropriate parametric or non-parametric test to make evidence-based conclusions about the population. Below is a rewritten, student-friendly overview that emphasises how each topic builds on the previous one , with clear illustrations, formulas, decision rules, and real-life examples suitable for undergraduate honours students. 1. Sampling Techniques & Sampling Distribution 🪚 Sampling Techniques: Sampling is the process of selecting a subset of individuals from a larger population to make statistical inferences. The goal is to obta...