A significant part of the statistical estimate is based on the assumption that the correct statistical model is estimated. The predominant reason for this practice is the widespread notion among academics that “statistically significant data is noteworthy, and one that is not statistically significant is not”. We may use these term interchangeably in the discussion below. Data dredging is recognized by several names such as ‘fishing trip’, ‘data snooping’, ‘p-hacking’ and so on. This may lead to an exponential increase in the risk of inclusion of large quantities of false positive results, thereby corrupting the data that was meant to be originally reported.
The following discussion will attempt to define data dredging and provide an answer to such questions. What is data dredging? How does it affect the p-value? What is its impact on the world around us?
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