Observer bias in epidemiology can manifest in various ways:
1. Data Collection: The observer's knowledge of the study participants' exposure status or disease status can influence how data is recorded. For instance, knowing a participant has been exposed to a risk factor might lead the observer to scrutinize their medical records more closely, thus identifying more cases of the disease.
2. Data Interpretation: An observer's preconceived notions or hypotheses can affect the way they interpret data. This can lead to overestimating or underestimating the association between an exposure and an outcome.
3. Reporting: The observer's beliefs may influence the way results are reported, selectively emphasizing findings that align with their expectations while downplaying or ignoring contradictory data.
