Epidemiology
The influence of certain factors on data analysis is always going to be a consideration (or at least should be) when studies are conducted, or projects undertaken. Researchers are considered to have bias under almost every circumstance, and as such should be reminded that the bias must be disclosed and considered when data is gathered and then analyzed. The validity of the data is also at stake; as one recent study determined "the validity of a (nonrandomized) observational study is often questioned because of unknown/unmeasured factors, which may have confounding and/or effect-modifying potential" (Wen-Chung, 2014, p. 1). So, determining the validity of the data is oftentimes seen as a way to measure the strength of the data overall. Therefore, of course conclusions of validity will be affected. Confounding takes place when the variables are not fully considered, so additional consideration...
When there is correlation between the external variables and the internal variables, then confounding takes place. The confounding leads to analysis that could be either under or over estimated. Either way affects the data analysis. As Wen-Chung says "Specifically, a perturbation is a variable that is readily available (or can be measured easily) and is potentially associated, though perhaps only very weakly, with unmeasured factors" (p. 2). Of course, perturbations are just as effective at affecting analysis as confounding.
Screening tests are quite effective in various medical forms, and especially in detecting colorectal cancer. A recent study determined that the most effective methods for screening for colorectal cancer would be through "annual fecal occult blood test (FOBT), flexible sigmoidoscopy, colonoscopy every 10 years, and/or double-contrast barium enema…
