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.
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Confidence Interval
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Content: 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. The inverse relationship between the confidence interval width and the certainty of prediction should be noted. In normal statistical analysis, the confidence interval tells us the reliability of the sample mean as compared to the whole mean. For example, in order to find out the average time spent by students of a university surfing the internet, one might take a sample student group of say 100, out of over 10,000 university students. From this sample mean, you can get the average time spent by that particular group. In order to be able to generalize this to the whole university group, you will need a confidence interval that reflects the applicability of this result for the given sample of students to the whole university.