Title: Understanding Hypothesis Tests: Confidence Intervals and Confidence Levels
Headings: Understanding Hypothesis Tests: Confidence Intervals and Confidence Levels
Understanding Hypothesis Tests: Confidence Intervals and Confidence Levels
How to Correctly Interpret Confidence Intervals and Confidence Levels
Confidence Intervals and the Margin of Error
Why P Values and Confidence Intervals Always Agree About Statistical Significance
Closing Thoughts

Content: "The parameter is an unknown constant and no probability statement concerning its value may be made." —Jerzy Neyman, original developer of confidence intervals. This will be easier to understand after we discuss the graph below . . . With this in mind, how do you interpret confidence intervals? Confidence intervals serve as good estimates of the population parameter because the procedure tends to produce intervals that contain the parameter. Confidence intervals are comprised of the point estimate (the most likely value) and a margin of error around that point estimate. The margin of error indicates the amount of uncertainty that surrounds the sample estimate of the population parameter. In this vein, you can use confidence intervals to assess the precision of the sample estimate. For a specific variable, a narrower confidence interval [90 110] suggests a more precise estimate of the population parameter than a wider confidence interval [50 150]. Confidence Intervals and the Margin of Error
Let’s move on to see how confidence intervals account for that margin of error.