Why are confidence intervals preferred over p-values?

Why are confidence intervals preferred over p-values?

• A. They provide a binary decision about significance
• B. They provide precision and the range of plausible values
• C. They require fewer assumptions about distributions
• D. They always indicate statistical significance

Answer: (B)

The main idea is that a confidence interval communicates both the size of the effect and how precisely we’ve estimated it. It provides a range of values that are plausible for the true parameter given the data, and the width of that range shows the study’s precision—the narrower the interval, the more precise the estimate. This is why it’s preferred over relying on a p-value. A p-value only tells whether the data would be unlikely if a specific null hypothesis were true; it doesn’t reveal how large the effect is or how uncertain we are about its value. It can be influenced by sample size and experimental design, and a non-significant result doesn’t prove there’s no real effect, while a significant result doesn’t indicate how big that effect is or whether it’s practically important. Confidence intervals fill in those gaps by showing the estimated effect and the range of values that are compatible with the data, enabling direct assessment of both magnitude and precision and making comparisons across studies more informative.

The main idea is that a confidence interval communicates both the size of the effect and how precisely we’ve estimated it. It provides a range of values that are plausible for the true parameter given the data, and the width of that range shows the study’s precision—the narrower the interval, the more precise the estimate.

This is why it’s preferred over relying on a p-value. A p-value only tells whether the data would be unlikely if a specific null hypothesis were true; it doesn’t reveal how large the effect is or how uncertain we are about its value. It can be influenced by sample size and experimental design, and a non-significant result doesn’t prove there’s no real effect, while a significant result doesn’t indicate how big that effect is or whether it’s practically important. Confidence intervals fill in those gaps by showing the estimated effect and the range of values that are compatible with the data, enabling direct assessment of both magnitude and precision and making comparisons across studies more informative.