Confidence Intervals and Their Implications

This video tutorial explains the concept of a 95% confidence interval, its calculation, and interpretation. It highlights the relationship between study size and confidence interval precision, and how confidence intervals can indicate statistical significance. The tutorial also discusses the implications of confidence intervals crossing zero and their role in comparing interventions. The video concludes with a note on the clinical relevance of results and suggests further resources for learning.

What does a 95% confidence interval represent?

• The certainty of the effect being positive.

• The likelihood of the study being repeated.

• The probability that the effect is exactly 95%.

• The range within which the true effect lies 95% of the time.

How does the number of participants affect the confidence interval?

• The number of participants does not affect the confidence interval.

• More participants lead to a smaller confidence interval.

• Fewer participants result in a narrower confidence interval.

• More participants lead to a wider confidence interval.

In a study with 1000 patients, what is the calculated confidence interval for a change of 14 degrees with a standard deviation of 5?

• 12.00 to 16.00

• 13.69 to 14.31

• 14.00 to 15.00

• 13.00 to 15.00

What happens to the confidence interval when the sample size is reduced to 25 patients?

• The confidence interval is unaffected by sample size.

• The confidence interval becomes narrower.

• The confidence interval becomes wider.

• The confidence interval remains the same.

What is the effect of choosing a lower confidence level, such as 90%?

• It results in a lower Z-value and a smaller interval.

• It results in a higher Z-value and a wider interval.

• It results in a higher Z-value and a smaller interval.

• It has no effect on the Z-value or interval size.

What does it mean if a confidence interval crosses zero?

• The intervention is more effective than another.

• The results are not statistically significant.

• The results are statistically significant.

• The intervention is effective.

Why is clinical relevance more important than statistical significance?

• Because statistical significance is always misleading.

• Because clinical relevance directly impacts patient care.

• Because clinical relevance is easier to calculate.

• Because statistical significance is not measurable.