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Correlation does not imply causation.įor example, global temperatures have steadily risen over the past 150 years and the number of pirates has declined at a comparable rate. That’s because this data fallacy is the false assumption that when two events occur together one must have caused the other. This can also be known as “cum hoc ergo propter hoc”, which is Latin for "with this, therefore because of this". To avoid falling for this fallacy, define your hypothesis upfront before analyzing data or testing for statistical significance. To avoid this, it’s now becoming standard practice to register clinical trials, stating in advance what your primary endpoint measure is. It explains why so many results published in scientific journals have subsequently been proven to be wrong. This resulted in them finding a spurious correlation between two variables that’s likely the result of chance.
![data dredging example data dredging example](https://image.slidesharecdn.com/thorntonbankdredginginternational-110322104340-phpapp02/95/deme-dredging-international-38-728.jpg)
Researchers have ‘data-dredged’ their results, repeatedly switching what they were testing for on a set of results.
![data dredging example data dredging example](https://image1.slideserve.com/1679416/what-is-dredging-l.jpg)
Tests for statistical significance only work if you’ve defined your hypothesis upfront.įor example, this has been a big problem with clinical trials. It’s the practice of repeatedly testing new hypotheses against the same set of data, failing to acknowledge that most correlations will be the result of chance. This is also sometimes known as data fishing, data snooping, or p-hacking.
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