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A better and increasingly common approach is to report confidence intervals.
The confidence intervals can be estimated for any chosen range.
Any relative risk with a confidence interval that does not include a value of 1 could be considered significant.
The confidence intervals increase as the observed number of cases decreases.
One approach is to estimate a confidence interval of the risk measurement.
This is necessary for the desired confidence interval property to hold.
Moreover, the confidence intervals found hold for a long-term prediction.
For example, a confidence interval can be used to describe how reliable survey results are.
Confidence intervals can be used to evaluate if a model is "close enough" to a system for some variable of interest.
Because of this problem several methods to estimate confidence intervals have been proposed.
For confidence intervals of difference scores, see Table 4.
For 10 samples the confidence interval would be 31 to 69 mg/l.
The confidence interval is always given together with the confidence level.
With the values in this example, the confidence interval is:
He introduced the confidence interval in his paper in 1937.
Although all genes have similar estimated fold changes, the confidence intervals are very different.
This can be used to obtain a confidence interval for .
Opinions of experts can be averaged, and the average mark is usually given with confidence interval.
Confidence intervals can then be derived from the tests.
These error bars are expressed as estimates of a confidence interval.
The dependence of the confidence intervals on sample size is further illustrated below.
For each of the pulmonary function measures, means and 95 percent confidence intervals were calculated.
Naturally, any assumptions required for the significance test would carry over to the confidence intervals.
The latter question is answered by the confidence intervals for the regression coefficients.
A sample size of 500 would have produced a confidence interval of 1.63.