Quantifying uncertainty is crucial for interpreting epidemiological findings. Common methods include:
Confidence Intervals: These provide a range within which the true effect size is likely to lie, offering a measure of precision. P-values: These indicate the probability that the observed results could have occurred by chance, helping to assess statistical significance. Sensitivity Analyses: These tests evaluate how robust the results are to changes in assumptions or key parameters. Monte Carlo Simulations: These computational techniques model the probability of different outcomes by running simulations multiple times.
