Some city health officials did not want to place food trucks at the Alcan City Fair. They felt that it did not add value to the fair and that it promoted unhealthy nutritional choices for the fair attendees. Could these opinions create bias in the studies that the city health officials conducted? Did the personal views of the Alcan City health officials influence the outcomes of their study and the interpretation of the data gathered?
Bias refers to deviations of results, or inferences, from the truth (Friis & Sellers, 2021). There are two overarching types of bias: information bias and selection bias. Both types can be detrimental to the validity and reliability of results. Several strategies exist to help prevent bias, but it is virtually impossible to eliminate bias altogether. In addition to bias, confounding variables can pose challenges for epidemiologists. Confounding is the masking of an association between an exposure and an outcome because of the influence of a third variable that was not considered in the study design or analysis. For example, if weight loss is the topic of study and exercise is the only variable considered, diet could mask the results of the study.
In this Discussion, you will review different types of bias, present an example of a study, and discuss whether bias was a factor in the study outcome. You will also discuss how the study design could have been altered to minimize or eliminate the risk of invalidating the results.
Reference:Friis, R.H., & Sellers, T.A. (2021). Epidemiology for public health practice (6th ed). Jones & Bartlett Learning.
To prepare for this Discussion:
Review the types of bias discussed in your textbook and listed below:
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