How Can Statistical Errors Lead to Misleading Conclusions?
Statistical errors can significantly impact the validity of epidemiological findings:
Type I and Type II Errors: A Type I error occurs when a true null hypothesis is incorrectly rejected, while a Type II error occurs when a false null hypothesis is not rejected. These errors can arise from inadequate sample sizes or inappropriate statistical tests. P-Hacking: This involves manipulating data or analyses until statistically significant results emerge. Such practices undermine the integrity of findings and lead to false-positive results. Overfitting: This occurs when a statistical model describes random error or noise instead of the underlying relationship. While the model may fit the sample data well, it performs poorly on new data.
