Statistics Module 1

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How could graphics and/or statistics be used to misrepresent data? Where have you seen this done? Statistics are of value to us all. As learned throughout the readings for this week, they are there to provide us vast amount of data related to health care, including why a drug may be indicated for a certain treatment (or the likelihood of it working); parameters for disease signs and symptoms; and even the prevalence of certain disease(s) within our areas to ensure competent care can be provided for our community. As noted by Nursing Research (2011): “Health information is very often explained in statistical terms for making it concise and understandable (Basic Statistical Concepts, 2011, para. 1).” As established, statistics play a large role in research!! The bad news is, just like anything, statistical data can be misrepresented. The misrepresentation of data can occur in a number of ways. It can occur unintentionally as a result of human error (in either data collection of graphical creation); or intentionally through bias misleading (Graphical Misrepresentation, 2006). Unintentional misrepresentation can occur through bad sampling, which is usually conclusive of sampling a small group in order to represent characteristics of a larger group or population (Basic Statistical Concepts, 2011). For example, this may be seen in presidential elections. Say 100 people in Indiana were asked who they were going to vote for during the past presidential election, and the majority said Romney (for this example sake, we will say 60 people said they would vote for Romney). This data could be turned around to represent the nation (or even just the state of Indiana) saying 60% of people plan to vote for Romney; when inevitably, we learned Obama took the win. Other examples of misrepresentation may include simply picking a bad color to demonstrate in a graph, or using a graph that is not suitable for the data being displayed (Graphical Misrepresentation, 2006)....
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