Lecture-0(part 2): From Classical to Bayesian Inference (Hypothesis testing)

Описание: 📘 Lecture 0 (Part 2): From Classical to Bayesian Inference – Hypothesis Testing

In this lecture, we continue the transition from Classical (Frequentist) Inference to Bayesian Inference by focusing on Hypothesis Testing under the classical framework.

Understanding hypothesis testing is essential before moving into Bayesian decision theory and posterior-based inference.

🔎 What You Will Learn:

✔️ Concept of statistical hypotheses (Null and Alternative)
✔️ Type I and Type II errors
✔️ Level of significance (α)
✔️ Test statistics and critical regions
✔️ p-value interpretation
✔️ One-tailed vs Two-tailed tests
✔️ Limitations of classical hypothesis testing
✔️ Motivation for Bayesian hypothesis testing

📊 Topics Covered:

• Z-test, t-test (conceptual discussion)

• Decision rules under classical framework
• Logical and practical limitations of p-values

🎯 By the End of This Lecture:

You will be able to:

Formulate null and alternative hypotheses

Understand error probabilities and power

Interpret p-values correctly

Apply likelihood ratio ideas

Recognize limitations that motivate Bayesian approaches

📚 Recommended for:
Statistics | Econometrics | Data Science | Research Scholars | Applied Mathematics | Quantitative Analysis

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