Understand sampling uncertainty
How confidence intervals express uncertainty
Confidence Interval for a Relative Risk. A confidence procedure adds model-based uncertainty around an estimate. Wider confidence levels and noisier or smaller samples generally produce wider intervals.
1Compute the sample estimate2Calculate its standard error3Apply the stated confidence procedure
Method used
RR=[a/(a+b)]/[c/(c+d)]; SE[log(RR)]=√(1/a−1/(a+b)+1/c−1/(c+d)).
The result compares observed risks and does not establish that exposure caused the outcome.
What the result cannot establish
A 95% confidence interval does not mean there is a 95% probability that a fixed parameter lies inside this realized interval. The 95% describes long-run coverage of the procedure under its assumptions.
Assumption: Groups must be independent with meaningful risk denominators; optional 0.5 correction is explicit for zero event cells.
Quick guide
How to use this calculator
- Enter the observations, probabilities, model parameters, or summary statistics requested by the visible labels.
- Keep every value on the same scale and confirm that the selected sampling relationship, distribution, and tail convention match the question you are investigating.
- Read the result together with its assumptions and interpretation. Statistical output summarizes uncertainty under a model; it does not repair biased data or establish causation.
Calculation method
How the confidence interval for a relative risk calculator works
RR=[a/(a+b)]/[c/(c+d)]; SE[log(RR)]=√(1/a−1/(a+b)+1/c−1/(c+d)).
The result compares observed risks and does not establish that exposure caused the outcome.
Worked example
Confidence Interval for a Relative Risk example
Risks 30/50 and 15/50 produce a relative risk of 2.
RR=[a/(a+b)]/[c/(c+d)]; SE[log(RR)]=√(1/a−1/(a+b)+1/c−1/(c+d)).
Supported inputs
Precision and limits
Model and design
Groups must be independent with meaningful risk denominators; optional 0.5 correction is explicit for zero event cells.
Numerical scope
Inputs use double-precision numerical methods with guarded domains. Datasets accept up to 10,000 finite plain-decimal values. Extremely large parameters or probabilities deep in a numerical tail may require specialist statistical software.
Interpretation
The result compares observed risks and does not establish that exposure caused the outcome.
Decision boundary
The calculator does not validate how data were collected, diagnose dependence or bias, choose a scientifically meaningful effect, or replace review by a qualified statistician for consequential research, medical, regulatory, safety, or policy decisions.
Privacy
Entered values and calculated results stay in this browser and are not sent to an analytics service.
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