Confidence Intervals: Why are they useful in helping to determine clinical significance? There are many controversies surrounding the issue of clinical significance vs. statistical significance. Identify one of them and summarize it. Finish with an opinion about the controversy. Please follow all the instructions in the questions. Please give one reference in APA format.

Confidence intervals are a statistical tool that provides a range of plausible values for a parameter of interest, such as a population mean or proportion. These intervals are useful in determining clinical significance because they allow researchers to estimate the true value of a parameter with a certain degree of certainty. By considering the range of values within the confidence interval, clinicians can assess whether the observed effect size is clinically meaningful or not.

One of the controversies surrounding clinical significance versus statistical significance is the reliance on p-values to make decisions. The traditional approach in hypothesis testing is to compare the p-value to a pre-defined significance level (often 0.05) to determine if the null hypothesis should be rejected. However, this binary decision based on a specific p-value threshold does not provide information about the magnitude or direction of the effect. As a result, statistical significance does not necessarily imply clinical significance.

For example, a study comparing a new drug to a placebo might find a statistically significant difference in the outcomes but with a small effect size. This means that although the results are unlikely to have occurred by chance, the observed difference may not be large enough to be clinically relevant. Patients and clinicians would be more interested in knowing the magnitude of the effect rather than a simple dichotomous decision based solely on p-values.

Another issue related to clinical significance is the arbitrary choice of the significance level. The conventional threshold of 0.05 is commonly used, but it does not have a strong scientific rationale. Setting the significance level at a different value, such as 0.01 or 0.10, can lead to different conclusions regarding statistical significance. This highlights the subjectivity involved in determining what is considered clinically meaningful and how statistical significance should be interpreted in the context of clinical trials.

In my opinion, the controversy surrounding clinical significance versus statistical significance calls for a more nuanced approach in interpreting research findings. Confidence intervals provide a valuable means to assess both statistical and clinical significance by incorporating uncertainty into the estimation of effect sizes. By looking at the range of plausible values within the confidence interval, researchers can better understand the potential impact of an intervention in a real-world setting.

Rather than solely relying on a fixed significance level, researchers should consider the practical importance of the effect size and the context in which the study is conducted. It is crucial to recognize that statistical significance alone does not guarantee clinical relevance. Therefore, a comprehensive evaluation of the estimated effect size, along with its precision and practical implications, is necessary to determine the clinical significance of the findings.

To further support my argument, a study by Button et al. (2013) examined the reporting of null hypothesis significance testing (NHST) in the biomedical literature. The authors found that NHST was often used as the sole decision-making tool, leading to the potential misinterpretation of results and inadequate consideration of clinical significance. The study highlights the importance of moving away from a strict reliance on p-values and encourages the use of confidence intervals to evaluate both statistical and clinical significance.

In conclusion, confidence intervals are invaluable in determining clinical significance as they provide a range of plausible values for a parameter of interest. The controversy surrounding clinical significance versus statistical significance emphasizes the need for a more nuanced approach in interpreting research findings. By integrating confidence intervals into the evaluation of effect sizes, researchers can better understand the potential clinical impact of interventions. A comprehensive assessment of the estimated effect size, along with its precision and practical implications, is crucial for determining clinical significance.