📊 Hypothesis Testing Intuition – Simplified for Everyone! 🔍 Ever wondered how data scientists make decisions based on data instead of assumptions? Welcome to the world of Hypothesis Testing — the backbone of statistical inference. 🎯 1. One-Sample t-Test 💡 Question: Is the average salary of employees in my company significantly different from the industry average (60,000)? ✅ Use When: You have one sample and want to compare it to a known population mean. 🎯 2. Two-Sample t-Test (Independent Samples) 💡 Question: Do male and female employees have different average salaries? ✅ Use When: You want to compare the means of two independent groups. 🎯 3. Paired t-Test 💡 Question: Did our employees' performance improve before vs after training? ✅ Use When: You compare two related samples (same group at two different times). 🎯 4. ANOVA (Analysis of Variance) 💡 Question: Do sales performance differ across 3 regions — North, South, East? ✅ Use When: You compare the means of 3 or more groups. 🎯 5. Chi-Square Test 💡 Question: Is there a relationship between customer gender and product preference? ✅ Use When: You're working with categorical variables to test for association or independence. 🎯 6. Z-Test 💡 Question: Is the average battery life of a new phone is greator than 10 hours (with known population standard deviation)? ✅ Use When: You know the population standard deviation and sample size is large (n is greator 30). 📌 Bonus: p-value Intuition p less 0.05 Evidence against the null hypothesis, result is statistically significant p greator 0.05 Not enough evidence, fail to reject the null