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The Problem of Auto-Correlation in Parasitology
Cherice Moore
ITT Technical Institute
GE257
12/17/2014

Parasitology is a branch of biology that deals with the study of parasites, their hosts, and the relationship between them. Parasitologists try to get an understanding of the many various factors that regulate the outcome of infections, and how these variables can actually adjust to a different intervention or some other type of external variation. Since infections are not simple, and have many different factors that can several different outcomes, parasitologists struggle to explain the relationship between the host and parasite in trying to control infections. The article, The Problem of Auto-Correlation in Parasitology, tries to illustrate how simple statistical models can not work for effective case studies because of many different ways that an in host environment can change and be manipulated by the parasite. The authors Laura Pollitt, Sarah Reece, Nicole Mideo, Daniel Nussey, and Nick Colegrave try to show how auto-correlation can result in biological errors that can cause statistical errors, and how mixed effect models can used to analyze repeated measures in data to have better statistical procedures in parasitology. (Politt, Reece, Mideo, Nussey, & Colegrave, 2012).
The article makes the claim that is now being acknowledged that having an analysis done that is based on only one single summary measure of an infection, for example, handwashing, is not an effective or appropriate way to acquire the many variations of an infection. Since are many variations that can occur within a host, which is the person or body that the parasite has invaded, using simple statistical measures can cause incorrect biological data. The article shows how there can be instances where an individual can be examined on many different occasions, and contain different

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