Title: How Hypothesis Tests Work: Confidence Intervals and Confidence Levels - Statistics By Jim
Headings: How Hypothesis Tests Work: Confidence Intervals and Confidence Levels
How Hypothesis Tests Work: Confidence Intervals and Confidence Levels
How to Interpret Confidence Intervals
Confidence Intervals Indicate the Precision of the Estimate
Creating Confidence Intervals Graphically
Confidence Intervals and the Inherent Uncertainty of Using Sample Data
Confidence Intervals and P Values Always Agree on Statistical Significance
Why They Always Agree
I Really Like Confidence Intervals!
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Content: See how confidence intervals compare to prediction intervals and tolerance intervals. Confidence Intervals Indicate the Precision of the Estimate
Confidence intervals include the point estimate for the sample with a margin of error around the point estimate. The point estimate is the most likely value of the parameter and equals the sample value. The margin of error accounts for the amount of doubt involved in estimating the population parameter. The more variability there is in the sample data, the less precise the estimate, which causes the margin of error to extend further out from the point estimate. Confidence intervals help you navigate the uncertainty of how well a sample estimates a value for an entire population. With this in mind, confidence intervals can help you compare the precision of different estimates. Suppose two different samples estimate the same population parameter with 95% confidence intervals. One interval is [5 15] while the other is [9 11]. The later confidence interval is narrower, which suggests that it is a more precise estimate.