If we cherry-pick results that suit our hypotheses, we may fail to consider the overall body of evidence. Lack of Reproducibility: Data dredging undermines the reproducibility of research.
Relying on such results can lead us down a misleading path and potentially harm scientific progress.
However, without a genuine underlying effect, this discovery is nothing but a false positive: a result that indicates the presence of something that actually isn’t there. If you think this sounds sneaky, you’re right!įalse Positives: By sifting through mountains of data, it’s almost inevitable that you’ll stumble upon a statistically significant result purely by chance. It’s like casting a wide net and selectively highlighting only the “winners” while disregarding the rest. Exciting, right? But before you break out the champagne, beware of the lurking danger called data dredging, also known as p-hacking or cherry-picking.ĭata dredging refers to the practice of exploring data exhaustively, trying out different combinations and analyses until a statistically significant result is found, purely by chance. You’re analyzing your study data, testing various hypotheses, and suddenly, you stumble upon a statistically significant result.
So, grab your lab coat and let’s embark on this enlightening journey together! What is Data Dredging? It’s time to separate fact from fiction and ensure that our research stays on the path of integrity. Have you ever found yourself swimming in a sea of data, desperately searching for a significant result? Well, hold on tight, because today we’re going to dive into the temptations of data dredging.
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