Epidemiologists control for variables such as confounding and random error by carefully developing research studies. Although there are not guarantees to eliminate or reduce all possible errors, it is...

Epidemiologists control for variables such as confounding and random error by carefully developing research studies. Although there are not guarantees to eliminate or reduce all possible errors, it is important to minimize their effects.
For this Discussion, review your readings from this week. Then addressing the following:
· Identify the role of p-values and confidence intervals within epidemiologic research and provide examples of how these are used to interpret research findings.
· Discuss the effect sample size has on p-values and confidence intervals and how this affects interpretation of statistical results.
It is important to design an experiment in epidemiology to eliminate differences due to confounding variables. Confounding occurs when the effects of other variable mixed up with an independent variable i.e. two associated variables have not been separated, resulting in the interpretation that the effect is due to one variable rather than the other. Confounding variables can result from either random error or bias. One of the possible ways to eliminate the differences is by controlling all the possible confounding variables. However, in practice it isn't logical to keep all the possible confounding variables to be fixed. In that case this can be done by statistically control them. The all possible confounding variables can be considered and the effects of these confounding variables can be separated...