Selectively removing outliers or dropping data points.Running multiple analyses and only reporting the significant ones. Collecting multiple measures and only reporting the significant ones.P-hacking can take many forms, including: This can be done intentionally or unintentionally, and the consequences can be significant. P-hacking, also known as data dredging or selective reporting, is a statistical practice that involves manipulating data or analysis in a way that increases the likelihood of finding a significant result. In this blog post, we’ll explore what p-hacking is, how it affects machine learning, and what steps organizations can take to avoid this pitfall. However, as with any statistical technique, machine learning is subject to pitfalls, one of which is p-hacking. Machine learning, a subset of artificial intelligence, has emerged as a powerful tool for analyzing data and making predictions. Digital transformation has revolutionized the way businesses operate, enabling organizations to leverage data and technology to gain insights and make better decisions.
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