• Interpretation: We are 95% confident that the true population parameter lies within the calculated interval.  
• Example: The researcher wants to estimate the average difference in blood pressure between patients taking the new drug and those taking the standard treatment. A 95% confidence interval might be (5 mmHg, 15 mmHg). This means we are 95% confident that the true average difference in blood pressure between the two groups is somewhere between 5 and 15 mmHg.

Key Differences Summarized:

Utilization in Healthcare Research:

Both hypothesis tests and confidence intervals are essential tools in healthcare research.

• Hypothesis tests: Used to evaluate the effectiveness of new treatments, compare the outcomes of different interventions, and identify risk factors for diseases.
• Confidence intervals: Used to estimate the prevalence of diseases, determine the accuracy of diagnostic tests, and assess the magnitude of treatment effects.

Workplace Example:

Let’s imagine a hospital is evaluating a new method for reducing patient wait times in the emergency room.

• Hypothesis Test: The hospital administrators might conduct a hypothesis test to determine if the new method significantly reduces wait times compared to the old method. The null hypothesis would be that there is no difference in wait times. The alternative hypothesis would be that the new method reduces wait times. The hypothesis test would produce a p-value. If the p-value is less than 0.05, they would reject the null hypothesis and conclude that the new method is effective in reducing wait times.  
• Confidence Interval: The hospital administrators might also calculate a 95% confidence interval for the average difference in wait times between the old and new methods. Let’s say the confidence interval is (-20 minutes, -5 minutes). This means they are 95% confident that the new method reduces wait times by between 5 and 20 minutes on average. This interval provides more information than just the p-value. It gives them a sense of the magnitude of the improvement.

How the Information is Used:

• Hypothesis test: Helps the hospital decide whether to implement the new method. If the test shows a statistically significant reduction in wait times, they are more likely to adopt the new method.
• Confidence interval: Helps the hospital understand the practical significance of the new method. Even if the reduction in wait times is statistically significant, it might not be clinically meaningful if the confidence interval suggests only a small reduction (e.g., a few minutes). The confidence interval helps them assess the clinical significance and make informed decisions about resource allocation. They might decide that a 5-minute reduction is not worth the cost of implementing the new method, even if it is statistically significant. However, a 20-minute reduction would be.  

In short, the hypothesis test answers the question: “Is there an effect?” The confidence interval answers the question: “How big is the effect?” Both pieces of information are crucial for making evidence-based decisions in healthcare.