p-Hacking
Definition
p-Hacking searches for statistically conspicuous results through flexible analyses.
The flexible trying out or selective reporting of analyses until statistically significant results emerge.
Simply explained
Many evaluations are tried, but only the one that looks convincing by chance is shown.
Why it matters in practice
The term helps to select, develop, evaluate AI systems in a well-founded manner and realistically assess their technical risks.
Example from business
Many segmentations are tested, but only the randomly significant variant is reported.
Limits and pitfalls
- Do not assume only intentional fraud; undisciplined analysis decisions can also cause the error.
Origin & history
p-Hacking has been investigated in replication and methodology research as a cause of biased literature.
Use in mid-sized companies
- Possible example: Many segmentations are tested, but only the randomly significant variant is reported.
Frequently asked questions
What does p-Hacking mean?
The flexible trying out or selective reporting of analyses until statistically significant results emerge. What mistakes should be avoided? Do not assume only intentional fraud; undisciplined analysis decisions can also cause the error.
Sources
Source checked on 10.10.2026. Editorial application examples are hypothetical; no evidence of real customer results.
Editorially reviewed on: