P-hacking can inflate Type I errors, wrongly rejecting true null hypotheses.P-hacking involves manipulating data or statistical analysis to produce false statistically significant results.Data Dredging: Data dredging is the failure to acknowledge that the correlation was in fact the result of chance. This practice can lead to an inflated rate of Type I errors, where a true null hypothesis is incorrectly rejected. The issue with p-hacking is its disregard for the principles of hypothesis testing. The critical threshold often lies at 0.05, below which results are statistically significant. P-hacking refers to the manipulation of ‘p-values,’ a standard statistical measure that tests the hypothesis probability given the observed data. It occurs when researchers consciously or unconsciously manipulate their data or statistical analyses until non-significant results become significant.

P-hacking, also known as data dredging or data snooping, is a controversial practice in statistics and data analysis that undermines the validity of research findings.

Data dredging is what kind of study