Several factors can lead to the misinterpretation of data in epidemiology:
1. Selection Bias: When the sample is not representative of the population. 2. Confounding Variables: Variables that can distort the true relationship between studied variables. 3. Measurement Error: Inaccurate data collection methods. 4. Overfitting: Creating models that are too complex and fit the noise rather than the signal. 5. Publication Bias: Favoring studies with positive results over those with null or negative findings.
