These are slides on a preliminary paper on "How many children, men, and women are living in poverty globally? An approach using structural and machine-learning models".

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Week 14 model critique analyzing Week 7 hypothesis testing lab. Creates digital marketing agency business scenario testing display ads vs. standard ads for 50+ e-commerce clients. Identifies critical statistical issues: untested assumptions (normality, equal variance), temporal dependence violating independence, and multiple comparisons inflation. Provides 5 improved analyses with R code: comprehensive assumption testing (Shapiro-Wilk, F-test, Q-Q plots), autocorrelation checks for temporal patterns, Bonferroni/Holm multiple testing corrections, practical significance framework with confidence intervals and business decision matrix, and post-hoc power analysis with sensitivity curves. Examines 3 ethical concerns: selection bias from unverified randomization (justice principle), cherry-picking metrics and HARKing enabling misleading results (scientific integrity), and economic harm from Type II errors with $2.7M opportunity cost calculation (harm minimization). Demonstrates that rigorous data science requires both technical proficiency and ethical reflection.

esto es una prueba propia KAMO