The article argues that overemphasis on hypothesis testing and P values in medical research has led to a neglect of more informative approaches, such as confidence intervals and estimation. Instead of focusing solely on whether results are statistically significant, researchers should aim to estimate the magnitude of the effect and present confidence intervals, which provide a range of values likely to contain the true population parameter. Confidence intervals are more informative than P values because they convey the precision of the estimate and allow for a better understanding of the study's findings. The paper discusses the limitations of P values, which are often used in isolation and can lead to misinterpretation. For example, a statistically significant result does not necessarily imply clinical or biological importance. Confidence intervals, on the other hand, provide a more comprehensive picture by showing the range of possible values for the population parameter and the precision of the estimate. The article provides methods for calculating confidence intervals for means, differences between means, and proportions. It also emphasizes the importance of reporting confidence intervals in both the main text and abstract of medical papers. The use of confidence intervals is recommended for major findings, as they provide more useful information than P values. However, in some cases, such as when data are purely descriptive, confidence intervals may not be appropriate. The paper also discusses the relationship between confidence intervals and hypothesis testing. A confidence interval that does not include zero indicates a statistically significant difference, while one that does include zero suggests no significant difference. The article provides examples of how confidence intervals can be used to interpret study results and highlights the importance of presenting both confidence intervals and P values for a complete understanding of the data. In conclusion, the article advocates for the use of confidence intervals in medical research as a more informative and useful approach to presenting study results. Confidence intervals provide a range of values that are likely to contain the true population parameter and allow for a better understanding of the study's findings. They should be used in place of P values whenever possible, as they provide more useful information and help avoid the pitfalls of overemphasizing statistical significance.The article argues that overemphasis on hypothesis testing and P values in medical research has led to a neglect of more informative approaches, such as confidence intervals and estimation. Instead of focusing solely on whether results are statistically significant, researchers should aim to estimate the magnitude of the effect and present confidence intervals, which provide a range of values likely to contain the true population parameter. Confidence intervals are more informative than P values because they convey the precision of the estimate and allow for a better understanding of the study's findings. The paper discusses the limitations of P values, which are often used in isolation and can lead to misinterpretation. For example, a statistically significant result does not necessarily imply clinical or biological importance. Confidence intervals, on the other hand, provide a more comprehensive picture by showing the range of possible values for the population parameter and the precision of the estimate. The article provides methods for calculating confidence intervals for means, differences between means, and proportions. It also emphasizes the importance of reporting confidence intervals in both the main text and abstract of medical papers. The use of confidence intervals is recommended for major findings, as they provide more useful information than P values. However, in some cases, such as when data are purely descriptive, confidence intervals may not be appropriate. The paper also discusses the relationship between confidence intervals and hypothesis testing. A confidence interval that does not include zero indicates a statistically significant difference, while one that does include zero suggests no significant difference. The article provides examples of how confidence intervals can be used to interpret study results and highlights the importance of presenting both confidence intervals and P values for a complete understanding of the data. In conclusion, the article advocates for the use of confidence intervals in medical research as a more informative and useful approach to presenting study results. Confidence intervals provide a range of values that are likely to contain the true population parameter and allow for a better understanding of the study's findings. They should be used in place of P values whenever possible, as they provide more useful information and help avoid the pitfalls of overemphasizing statistical significance.