Hypothesis Testing 📊 | Inferential Statistics Part-2| 30 Days of Data Analytics

📊 *Day 16 – Hypothesis Testing | 30 Days of Data – Data Analytics Bootcamp* Welcome back to the *30 Days of Data – Data Analytics Bootcamp!* 🚀 In Day 16, we continue learning *Inferential Statistics* and dive into Hypothesis Testing — a fundamental statistical technique used to test claims, validate assumptions, and draw conclusions from sample data. 🎯 *What You'll Learn:* ✅ Introduction to Hypothesis Testing ✅ What is a Null Hypothesis (H₀)? ✅ What is an Alternative Hypothesis (H₁)? ✅ Understanding the hypothesis-testing process ✅ Visualizing the normal distribution and rejection regions ✅ Understanding significance level (α) ✅ Critical regions and statistical decision-making ✅ Rejecting or failing to reject the null hypothesis 📈 *Why is Hypothesis Testing Important?* Hypothesis testing helps us determine whether the evidence from a sample provides sufficient support against a claim about a population. It is widely used in data analytics, data science, scientific research, and business decision-making. By understanding this concept, you'll build a stronger foundation in statistics and learn how data can be used to evaluate assumptions systematically. 🚀 *About This Series – 30 Days of Data* This bootcamp is a structured learning journey covering Python, NumPy, Pandas, data cleaning, statistics, data visualization, exploratory data analysis, SQL, and more. 📌 Follow along, practice consistently, and build your data analytics skills one day at a time! 👍 Like the video if you find it useful. 💬 Comment your questions and doubts below. 🔔 Subscribe for more data analytics learning content. #HypothesisTesting #InferentialStatistics #DataAnalytics #Statistics #DataScience #30DaysOfData #LearnDataAnalytics