Data dredging (also called p-hacking) is the statistical manipulation of data in order to find patterns which can be presented as statistically significant, when in reality there is no underlying effect.
This can be achieved in a number of ways, such as:
Excluding certain participants
Stopping data collection once a p value of 0.05 is reached
Analysing many outcomes, but only reporting those with p < 0.05
The reason for this practice is the widespread notion in the academic community that only statistically significant findings are noteworthy. This idea leads to publication bias.