What is p-value fishing?

What is p-value fishing?

P-value hacking, also known as data dredging, data fishing, data snooping or data butchery, is an exploitation of data analysis in order to discover patterns which would be presented as statistically significant, when in reality, there is no underlying effect.

How can P-hacking be reduced?

Preventing P-Hacking

1. Decide your statistical parameters early, and report any changes.
2. Decide when to stop collecting data and what composes an outlier beforehand.
3. Correct for multiple comparisons, and replicate your own result.

Why is p hacking a misuse of data?

This is a technique known colloquially as ‘p-hacking’. It is a misuse of data analysis to find patterns in data that can be presented as statistically significant when in fact there is no real underlying effect. Why it matters? Most scientists are careful and scrupulous in how they collect data and carry out statistical tests.

How does harking result from multiple comparisons and p-hacking?

HARKing typically results from multiple comparisons and p-hacking, although technically it doesn’t have to (i.e., the analysts got lucky on the first shot and didn’t do anything else).

How is data dredging related to p-hacking?

Data dredging (or data fishing, data snooping, data butchery), also known as significance chasing, significance questing, selective inference, and p-hacking is the misuse of data analysis to find patterns in data that can be presented as statistically significant, thus dramatically increasing and understating the risk of false positives.

What do you mean by p hacking in science?

This piece introduces one such technique known as ‘p-hacking’. It is one of the most common ways in which data analysis is misused to generate statistically significant results where none exists, and is one which everyone reporting on science should remain vigilant against.