Title: Statistics Confidence Interval - Reliability of an Estimate
Headings: Confidence Interval in Statistics
Confidence Interval in Statistics
In statistics, confidence interval refers to the amount of error that is allowed in the statistical data and analysis.
One Tailed
Two Tailed
Confidence Interval
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Content: One Tailed
>.95 (>95%)
>.99
>.999
Two Tailed
>.975 and <.0275
Confidence Interval
Since statistics uses a sample space and predicts the trends for the whole population, it is quite natural to expect a certain degree of error and uncertainty. This is captured through the confidence interval. You will frequently encounter this concept while looking at survey results, which take the data of a few people and extend it to the whole group. Suppose the survey shows that 34% of the people vote for Candidate A. The confidence that these results are accurate for the whole group can never be 100%; for this the survey would need to be taken for the entire group. Therefore if you are looking at say a 95% confidence interval in the results, it could mean that the final result would be 30-38%. If you want a higher confidence interval, say 99%, then the uncertainty in the result would increase; say to 28-40%. The confidence interval depends on a variety of parameters, like the number of people taking the survey and the way they represent the whole group. For most practical surveys, the results are reported based on a 95% confidence interval.