SEMANA 5 — ANÁLISIS COMPLETO DEL DCA: INTERPRETACIÓN, SUPUESTOS Y PRUEBAS POST HOC

SEMANA 4 — DISEÑO COMPLETAMENTE ALEATORIZADO (DCA)

This notebook continues the analysis of the World Bank Statistical Performance Indicators (SPI) dataset, a longitudinal country-level dataset covering 217 countries from 2004 to 2023. Each row represents one country-year observation and includes multiple measures of statistical capacity, such as data use, production, and infrastructure. This week, hypothesis testing is used to examine whether meaningful differences in statistical performance exist between income groups. Specifically, AB testing compares High income countries (Group A) and Low income countries (Group B) across two performance indicators. Two hypothesis testing frameworks are applied. Hypothesis 1 uses the Neyman–Pearson framework, which involves pre-specified error rates, power analysis, and a reject or fail-to-reject decision. Hypothesis 2 uses Fisher’s significance testing framework, which focuses on interpreting the p-value and assessing the strength of evidence against the null hypothesis. Understanding the relationship between income level and statistical capacity has policy relevance, as it may inform decisions related to development funding, technical assistance, and governance priorities.