P-Hacking - Crash Course Statistics

The video discusses the concept of p-hacking, where data is manipulated to achieve significant p-values, and its impact on research credibility. It explains p-values, null hypothesis testing, and the consequences of p-hacking, using real-world examples. The video also covers multiple testing, family-wise error rates, and methods like the Bonferroni correction to ensure valid research results.

What are the consequences of publishing non-significant results?

Discuss the importance of transparency in statistical testing.

What is the Bonferroni correction and how is it applied?

How can misleading results affect public perception and policy?