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
Aditi Sharma | Sciencx (2025-09-24T10:13:39+00:00) 🚀 Day 24 of My Python Learning Journey. Retrieved from https://www.scien.cx/2025/09/24/%f0%9f%9a%80-day-24-of-my-python-learning-journey/
Please log in to upload a file.
There are no updates yet.
Click the Upload button above to add an update.