Lesson 1128 of 1570
AI and spotting fake studies: predatory journals and made-up stats
AI helps you sniff out predatory journals, fake citations, and made-up statistics.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The big idea
- 2predatory journal
- 3p-hacking
- 4retracted
Concept cluster
Terms to connect while reading
Section 1
The big idea
Some 'research' is in fake academic journals, some uses bad stats, and some has been retracted. AI can help you check before you cite a study that turns out to be junk in your paper.
How to use it
- Ask AI to check if a journal is on the Beall's List of predatory pubs
- Ask AI to flag retraction status of a paper
- Ask AI to explain p-hacking and what it looks like in a study
- Ask AI to find the retraction notice if there is one
Try it
Find a study cited in a viral post. Ask AI to check the journal, retraction status, and sample size. Report what you find.
Key terms in this lesson
End-of-lesson quiz
Check what stuck
15 questions · Score saves to your progress.
Tutor
Curious about “AI and spotting fake studies: predatory journals and made-up stats”?
Ask anything about this lesson. I’ll answer using just what you’re reading — short, friendly, grounded.
Progress saved locally in this browser. Sign in to sync across devices.
Related lessons
Keep going
Builders · 40 min
Primary Sources vs Secondary Sources
A primary source is the original — the first-hand account or original data. A secondary source describes or analyzes a primary source. Smart researchers use both, but they know the difference.
Builders · 7 min
AI and source triangulation: never trust a single source again
AI helps you check a fact across 3 sources before you cite it in any paper.
Builders · 7 min
AI and survey question design: stop accidentally biasing your data
AI helps you write survey questions that don't lead respondents to the answer you want.