The article discusses the prevalence and consequences of p-hacking in scientific research. P-hacking refers to the practice of manipulating data or statistical analyses to produce significant results, often by collecting or selecting data until nonsignificant results become significant. The authors use text-mining to demonstrate that p-hacking is widespread across various scientific disciplines. They also test for p-hacking in meta-analyses, finding that while p-hacking is likely common, its effect on the estimated effect sizes seems weak. This suggests that p-hacking may not significantly alter the conclusions drawn from meta-analyses. The article highlights the importance of addressing p-hacking to improve the reliability and reproducibility of scientific findings. Recommendations for researchers and journals are provided to reduce the occurrence of p-hacking, including better education, adherence to common analysis standards, and clearer reporting guidelines.The article discusses the prevalence and consequences of p-hacking in scientific research. P-hacking refers to the practice of manipulating data or statistical analyses to produce significant results, often by collecting or selecting data until nonsignificant results become significant. The authors use text-mining to demonstrate that p-hacking is widespread across various scientific disciplines. They also test for p-hacking in meta-analyses, finding that while p-hacking is likely common, its effect on the estimated effect sizes seems weak. This suggests that p-hacking may not significantly alter the conclusions drawn from meta-analyses. The article highlights the importance of addressing p-hacking to improve the reliability and reproducibility of scientific findings. Recommendations for researchers and journals are provided to reduce the occurrence of p-hacking, including better education, adherence to common analysis standards, and clearer reporting guidelines.