De Anza College Global Environment Obesity Discussion

Description

In this DQ you’re going to talk about lying with data.

The way you communicate epidemiological findings matters. For example, in your reading, you’ll learn about the perils of reporting relative risk (RR) in cased where population attributable risk (PAR) would make more sense.

Another way that epidemiologists (or the people they work for) can mislead people is with graphics / data visualizations that obfuscate instead of enlightening. If you spend any time watching the news or following the COVID-19 pandemic on social media, you will have seen many examples of this (whether you noticed them or not).

Prompt: For this DQ, find a health-related example of misleading data visualization. It doesn’t have to be related to COVID-19; it can be anything in the health domain. Briefly describe 1) The health issue and why it’s important, 2) The REALITY of the data, 3) How and why the data visualization is misleading, and 4) How the problem(s) with the data visualization could be fixed.

As always, please remember to respond to two of your classmates for your follow-on participation.

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