The article "Moving beyond P values: Everyday data analysis with estimation plots" by Joses Ho, Tayfun Tumkaya, Sameer Aryal, Hyungwon Choi, and Adam Claridge-Chang discusses the limitations of null-hypothesis significance testing (NHST) and advocates for the use of estimation plots as a more comprehensive and intuitive method for data analysis. The authors argue that NHST focuses on dichotomous thinking and fails to provide a complete picture of the data, particularly in the context of two-groups designs. They introduce estimation plots, which display both observed values and effect sizes, along with their uncertainty, to enhance transparency and provide a more quantitative understanding of the data. The article highlights five key advantages of estimation plots over traditional NHST plots, including improved transparency, better representation of precision, and the ability to avoid dichotomous thinking. To facilitate the adoption of estimation plots, the authors have developed DABEST, a free software package available in Matlab, Python, and R, along with a user-friendly web application. The article also demonstrates the versatility of estimation plots through various experimental designs and statistical analyses, emphasizing their broad relevance in scientific research.The article "Moving beyond P values: Everyday data analysis with estimation plots" by Joses Ho, Tayfun Tumkaya, Sameer Aryal, Hyungwon Choi, and Adam Claridge-Chang discusses the limitations of null-hypothesis significance testing (NHST) and advocates for the use of estimation plots as a more comprehensive and intuitive method for data analysis. The authors argue that NHST focuses on dichotomous thinking and fails to provide a complete picture of the data, particularly in the context of two-groups designs. They introduce estimation plots, which display both observed values and effect sizes, along with their uncertainty, to enhance transparency and provide a more quantitative understanding of the data. The article highlights five key advantages of estimation plots over traditional NHST plots, including improved transparency, better representation of precision, and the ability to avoid dichotomous thinking. To facilitate the adoption of estimation plots, the authors have developed DABEST, a free software package available in Matlab, Python, and R, along with a user-friendly web application. The article also demonstrates the versatility of estimation plots through various experimental designs and statistical analyses, emphasizing their broad relevance in scientific research.