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Confidence Intervals

The confidence interval may be thought of as the range of probable true values for a statistic. When public health practitioners use health statistics, sometimes they are interested in the actual number of health events, but more often they use the statistics to assess the underlying risk in the community. Observed measures of health events, that is, counts, rates or percentages that are calculated from health surveys, vital statistics data or other health surveillance systems, are not a perfect reflection of the true underlying risk in the population. Observed rates can vary from sample to sample or year to year, even when the true underlying risk is identical. Rates that fluctuate widely in the absence of changes in the true underlying risk are called unstable, imprecise, or unreliable.

Contents
1 Use of Confidence Intervals
2 Technical Definition
3 Calculation

References

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Use of Confidence Intervals

The confidence interval is an indication of the stability of the statistical estimate. In general, as a population (or sample) size increases, the confidence interval gets smaller, indicating that the estimate is more stable. Conversely, wider confidence intervals indicate less stable estimates. Estimates calculated from small numbers will have wider confidence intervals.

The confidence interval tells you more than just the possible range around the estimate. It also tells you about the stability of the estimate. A stable estimate is one that would be close to the same value if the observation were repeated. An unstable estimate is one that would vary from one observation to another. Wider confidence intervals in relation to the estimate itself indicate instability.

info icon Even for complete count datasets, such as birth and death certificate datasets, random fluctuations over time will yield estimates that are not reliable. For instance, the death rate for a short time period from a small population will not reflect the true underlying death risk for that population.


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Technical Definition


The 95% confidence interval (calculated as 1.96 times the standard error of a statistic) indicates the range of values within which the statistic would fall 95% of the time if the researcher were to calculate the statistic from an infinite number of samples of the same size drawn from the same base population. Unless otherwise stated, a confidence interval will be the "95% confidence interval." The 90% confidence interval is also commonly used. The 90% confidence interval is calculated as 1.65 times the standard error of the estimate.

info icon A confidence interval is typically expressed as a symmetric value (e.g., "plus or minus 5%"). But for percentages, when the point estimate is close to 0% or 100%, the confidence interval will assume an asymmetric shape. That is, when the point estimate is close to 100%, the upper confidence bound will be smaller (so the confidence interval upper bound will not exceed 100%), and for point estimates close to 0%, the lower confidence bound will be smaller (so the confidence interval lower bound will not be lower than 0%). The formula that produces asymmetric confidence intervals has been applied to all the survey estimates in the IBIS query system.

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Calculation


For more information on confidence intervals, including formulae for calculating them for various types of rates, a PDF document is available.


Proceed to the page on measurement reliability and validity.



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The information provided above is from the Office of Epidemiology and Scientifict Support, Montana Department of Public Health and Human Services MT-IBIS web site (http://dphhs.mt.gov/). The information published on this website may be reproduced without permission. Please use the following citation: " Retrieved Sat, 19 October 2019 0:50:15 from Office of Epidemiology and Scientific Support, Montana Department of Public Health and Human Services, Indicator-Based Public Healt Information System for Public Health Web site: http://dphhs.mt.gov/ ".

Content updated: Fri, 3 Jun 2016 16:37:53 MDT