A fishing expedition is the indiscriminate testing of associations between different combinations of variables not with specific hypotheses in mind but with the hope of finding something that is statistically significant in the data. P-hacking is the relentless analysis of data with an intent to obtain a statistically significant result, usually to support the researcher's hypothesis. Cherry-picking is the presentation of favorable evidence with the concealment of unfavorable evidence. HARKing (Hypothesizing After the Results are Known) is the presentation of a post hoc hypothesis as an a priori hypothesis. You have just fished for data.Questionable research practices (QRPs) in the statistical analysis of data and in the presentation of the results in research papers include HARKing, cherry-picking, P-hacking, fishing, and data dredging or mining. 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. 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. 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. 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.
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