Super Data Science: ML & AI Podcast with Jon Krohn 303: Proper Hypothesis Testing For Every Field
Oct 9, 2019
Chapters
Transcript
Episode notes
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
Introduction
00:00 • 5min
The Year of the Nobel Prize Winner
05:24 • 2min
The Nobel Prize in Physics
07:41 • 2min
The Evolution of Dark Energy
09:45 • 3min
The Uncertainty Principle in Quantum Mechanics
12:55 • 2min
How I Become an Australian Survivor
14:29 • 3min
The Challenges of Being an Academic
17:24 • 2min
Python for Statistics Course Launches on Udemy and SDS
19:23 • 3min
The Importance of Graphic Exploration in Statistics
22:01 • 3min
Python vs R: The Future of Astrophysics
24:32 • 2min
Python vs. R: Which Is the Better Option?
26:22 • 2min
Python and the Future of Data Science
28:25 • 4min
How to Quantify Election Interference
32:15 • 4min
The Null Hypothesis and the H1 Hypothesis
35:58 • 4min
The Null Hypothesis in Astrophysics
39:30 • 2min
Why You Should Care About Statistical Significance in Data Science
41:45 • 3min
The Importance of P Values in Science
44:44 • 4min
The Importance of Multiple Methods in Astrophysics
48:39 • 2min
The Difference Between Frequentist and Bayesian Statistics
50:38 • 2min
The Differences Between Bayesian Statistics and Frequent Statistics
53:02 • 3min
The Benefits of Bayesian Statistics
55:54 • 3min
How to Learn Bayesian Statistics
59:00 • 3min
The Future of Physics
01:02:23 • 2min
How to Get a Job in Astrophysics
01:04:00 • 2min
Bayesian Methods in Cosmology
01:05:52 • 4min