What p-hacking really looks like: A comment on Masicampo LaLande (2012)

Key Points

• P-hacking leads to an increase in Type 1 error rates across the 0.00-0.05 range, not just near .05.
• The commentary challenges existing notions of p-hacking, emphasizing its broader impacts on statistical significance.
• This analysis underscores the importance of understanding the interpretation of p-values in research.
• Implications of p-hacking extend beyond isolated studies, affecting the overall literature landscape.