Master causal comparative (ex-post-facto) research design — its definition, procedure, data analysis techniques (t-test, ANOVA, chi-square), and comparison with correlational and experimental designs.
Comprehensive guide to the major parametric statistical tests—t-tests for two samples, ANOVA for three or more samples, and Pearson's r and Fisher's Z for correlation—including formulas, assumptions, and worked examples.
Comprehensive comparison of t-tests with their non-parametric equivalents — Mann-Whitney U and Wilcoxon MPSR — covering power efficiency, assumptions, and practical decision frameworks for psychology research.
Comprehensive answers to all previous year questions on t-Test in MPC-006 Statistics in Psychology (10 papers, 2011–2024)
Apply t-tests to small independent and correlated samples, understand Type I and Type II errors, and follow the step-by-step process for significance testing in psychological research.
Master the theory and formulas for testing whether two sample means differ significantly — covering null hypothesis, critical ratio, levels of significance, and one- vs two-tailed tests.