You would like to show progress, so you decide to include them in your report to management. Your company has no business in Ohio or Louisiana, and it there is no reasonable conclusion why these correlations exist, even though the p-values are less than 5%. Of the 100, two show statistical significance: interest rates on boat loans in south Louisiana and household debt in north Ohio. You download a database from a government website and decide to run a series of automated regressions. Imagine that instead of choosing only four macroeconomic indicators, you chose 100. Real-time data on mixture production or dredger position will optimise operational efficiency of any dredging project. This could have gone another way…īUT wait! This could have gone another way.

An honest analyst would not make this claim, but show that there is no relationship. In this case, the assumed statistical significance is false, that is significance tests do not protect conclusions against data fishing (Roddick and others, 2003). You had conflicting evidence of the statistical significance of two correlations, but since one side of the evidence supports a personal claim you would like to make, you decide to accept it. Data fishing (dredging or mining) is the inapt search for statistically significant relationships in a data set whether intentional or unintentional because of badly chosen procedure.

Since you’re in a bind, you decide to claim that GDP and Employment rate have a strong relationship to revenue, and that you’re going to explore this further.

, data dredging