Trainer 1203.1: Hypothesis Testing in a Changing World

Hypothesis testing is a fundamental concept in statistical analysis, guiding researchers in making data-driven decisions. This course explores the principles and applications of hypothesis testing in an evolving landscape influenced by emerging trends in science, technology, and global challenges. Participants will gain insights into traditional and modern hypothesis testing approaches, the impact of artificial intelligence on statistical inference, and strategies to address biases and uncertainties in decision-making. Learning Outcomes 1. To understand the fundamental principles of hypothesis testing. 2. To be able to learn how to formulate null and alternative hypotheses. 3. To explore different types of hypothesis tests, including parametric and non-parametric methods. 4. To assess the role of p-values, confidence intervals, and effect sizes in hypothesis testing. 5. To analyze real-world datasets using hypothesis testing techniques. 6. To evaluate the impact of evolving scientific and technological advancements on hypothesis testing. 7. To develop skills in addressing biases and limitations in statistical inference. 8. To learn how to apply hypothesis testing across various disciplines. 9. To understand ethical considerations in hypothesis testing and data-driven decision-making. 10. To integrate AI-driven tools in hypothesis testing for enhanced accuracy and efficiency.