🚀 Day 26 of My Python Learning Journey

#programming #python #database #productivity

Hypothesis Testing in Data Analysis 📊

Today I explored the backbone of statistical decision-making → Hypothesis Testing.

🔹 Key Steps:
1. State Hypotheses
• Null (H₀): No effect/difference
• Alternative (H₁): There is an effect/difference
2. Set Significance Level (α) → usually 0.05
3. Choose a Test → t-test, chi-square, ANOVA, etc.
4. Calculate Test Statistic & P-Value
5. Make a Decision
• If p < α → Reject H₀ ✅
• Else → Fail to reject H₀

🔹 Why it matters?

✅ Validates assumptions
✅ Minimizes bias in decisions
✅ Powers research, A/B testing & analytics

⚡ Fun Fact: Hypothesis testing was formalized by Ronald Fisher & Jerzy Neyman in the early 1900s — and it’s still the gold standard in science!