🚀 Day 24 of My Python Learning Journey

P-Value & Critical Region in Probability 📊

Today I explored two important concepts in hypothesis testing:

🔹 P-Value
• Probability of getting results at least as extreme as observed, assuming the null hypothesis is true.
• Low p-value (< 0.05) …


This content originally appeared on DEV Community and was authored by Aditi Sharma

P-Value & Critical Region in Probability 📊

Today I explored two important concepts in hypothesis testing:

🔹 P-Value
• Probability of getting results at least as extreme as observed, assuming the null hypothesis is true.
• Low p-value (< 0.05) → strong evidence against null hypothesis.

🔹 Critical Region
• The range of values where we reject the null hypothesis.
• Defined by significance level (α), often 5%.

🔹 Why it matters?

âś… P-value tells us how surprising our result is.
âś… Critical region decides whether to accept or reject a hypothesis.

⚡ Fun Fact: The 0.05 threshold for p-values was first popularized by Ronald Fisher in the 1920s — and it still rules data science & research today! 📖

Python #Statistics #Probability #100DaysOfCode #DataAnalytics


This content originally appeared on DEV Community and was authored by Aditi Sharma


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