Clinically relevant statistics are used to

Clinically relevant statistics are used to

• A. Determine statistical significance only
• B. Evaluate the importance of outcomes for patient care
• C. Describe the distribution of a variable
• D. Compute p-values

Answer: (B)

Clinically relevant statistics focus on the practical impact of study findings on patient care. They translate numbers into what matters for patients, such as how big a benefit is in real terms, how certain we are about that benefit, and what the balance of benefits and harms means for decision making. This includes measures like absolute and relative risk reductions, number needed to treat, and the minimal clinically important difference, which help clinicians decide whether a therapy is worth using in practice. A result can be statistically significant yet clinically trivial if the actual benefit is small or uncertain in real-world terms, while the goal of clinically relevant statistics is to prioritize outcomes that meaningfully affect patient health and care decisions. Describing distributions or computing p-values speaks to data properties and statistical inference, not to the practical importance of the findings for patients.

Clinically relevant statistics focus on the practical impact of study findings on patient care. They translate numbers into what matters for patients, such as how big a benefit is in real terms, how certain we are about that benefit, and what the balance of benefits and harms means for decision making. This includes measures like absolute and relative risk reductions, number needed to treat, and the minimal clinically important difference, which help clinicians decide whether a therapy is worth using in practice. A result can be statistically significant yet clinically trivial if the actual benefit is small or uncertain in real-world terms, while the goal of clinically relevant statistics is to prioritize outcomes that meaningfully affect patient health and care decisions. Describing distributions or computing p-values speaks to data properties and statistical inference, not to the practical importance of the findings for patients.