BCS 205 Chapters 13 & 14 – 50 Q&A on Statistical Tests, Effect Sizes, Errors & Correlation Methods

This document contains 50 well-organized, expert-verified practice questions and answers for BCS 205, specifically covering Chapters 13 and 14. It serves as a high-impact revision tool for students studying intermediate to advanced statistics within behavioral, cognitive, or social science programs. Each question is designed to reinforce key concepts and promote deep understanding of data interpretation and analysis techniques. Covered topics include descriptive statistics (central tendency, dispersion, kurtosis), correlation measures (Pearson’s r, Spearman’s rho, Cramer's V, Phi, Point-Biserial), and effect size calculations such as Cohen’s d and Eta Squared. The document also explains statistical testing procedures—both parametric (e.g., t-tests, ANOVA, linear regression) and nonparametric alternatives (e.g., Wilcoxon, Mann-Whitney U, Friedman, Kruskal-Wallis). Additionally, it covers error types (Type I, Type II), common statistical fallacies, and key assumptions like sphericity and normality tests (Shapiro-Wilk, Kolmogorov-Smirnov, Levene’s test). This material is especially suited for: Undergraduate and graduate students in Statistics, Psychology, Sociology, Neuroscience, Data Science, and Research Methods Learners preparing for quizzes, midterms, or finals in behavioral research, experimental design, or quantitative methods Students needing clear explanations of core statistical tests and their assumptions for lab reports or theses Whether you’re reviewing concepts like effect size interpretation or comparing statistical testing methods, this document delivers a concise and reliable foundation. Keywords: BCS 205, descriptive statistics, data cleaning, central tendency, Pearson’s r, Spearman’s rho, Cohen’s d, point biserial, ANOVA, t-test, Welch’s test, Mann Whitney U, Kruskal Wallis, Friedman test, effect size, type 1 error, type 2 error, Shapiro Wilk test, Levene’s test, statistical fallacies, linear regression, omnibus hypothesis, interaction effects, odds ratio