P-values are the statistical equivalent of a Rorschach test - everyone sees what they want to see. A 2015 survey of 1,576 researchers found that 51% misinterpret p-values as effect size indicators. Here's how to spot misuse:
Studies show papers with p=0.049 are 4x more likely to be published than p=0.051. This is called p-hacking.
Treating p<0.05 as "true" and p>0.05 as "false" is like saying a 49% chance of rain means it won't rain.
Running 20 tests and reporting only the significant one? That's like buying 20 lottery tickets and claiming you're rich because one won $5.
Real-time feedback: If you see these patterns, sound the alarm!