Explain the two major types of bias. Identify a peer-reviewed epidemiology article that
discusses potential issues with bias as a limitation and discuss what could have been done to
minimize the bias (exclude articles that combine multiple studies such as meta-analysis and
systemic review articles). What are the implications of making inferences based on data
with bias? Include a link to the article in your reference.

Explain the two major types of bias

Bias can be stated to be a systematic error that occurs in any form of epidemiological
studies and it always results in an incorrect estimation in the relationship between outcomes and
exposures. There are two forms of bias which include selection bias and information bias.
Selection bias can be stated to be a sample taken for a given research which does not reflect the
general population in an accurate manner. For instance, when one is to conduct research on the
level of obesity but only uses a sample of the upper-class children from a given region in
America. On the other hand, information bias can be stated to be a bias that results due to errors
that occur in measurements. It can also be argued to be a situation in which crucial information is
collected, measured or interpreted in an inaccurate manner.
A peer-reviewed epidemiology article and how to minimize the bias
A study by Reveiz et al. (2017) is the article identified to contain issues with bias as a
limitation. The first ways to minimize bias in the article is to use multiple individuals to code the
data used in the research. In such a situation there will be consistency thus accurate data and
reduction of bias. The second way would be conducting a data verification process on the


sources. This is triangulation which entails finding another sort of data that support the
researcher`s interpretation.
The implications of making inferences based on data with bias?
The effects of using biased data to make an inference are that the conclusion that is made
from the study will not be accurate and this might mislead scholars about a given issue. Thus it
will erroneously attribute a given phenomenon.

Reveiz, L., Haby, M. M., Martínez-Vega, R., Pinzón-Flores, C. E., Elias, V., Smith, E., … & Van
Kerkhove, M. D. (2017). Correction: Risk of bias and confounding of observational
studies of Zika virus infection: A scoping review of research protocols. PloS one, 12(11),