# Stats DQs Essay

Submitted By k3tess
Words: 1526
Pages: 7

Please make sure that you give a COMPLETE response to all DQs asked, IN YOUR OWN WORDS (minimum 200, excluding citations), along with proper CITATIONS. If any of these are missing, you will not get full points for DQs.
Your follow up posts should be at least 100 words, and MUST be in your own words.
Textbook:
Grove, S. K. (2007). Statistics for health care research: A practical workbook. Edinburgh: Elsevier Saunders.
Topic 1 DQ 1: How can graphics and/or statistics be used to misrepresent data? Where have you seen this done?
Statistics are used everywhere, every day to represent a multitude of data, or study sample. Sample characteristics are the traits that depict the study sample and can be portrayed in either some type of table or in an article (Grove, 2007). “Descriptive statistics are used to generate sample characteristics, and the type of statistic used depends on the level of measurement or the demographic variables included in the study” (Grover, 2007, p.75). It is this information and data that is presented can be misrepresented, either unethically or simply because the data is misunderstood. According to Statistics (2013), data can be misrepresented three ways: 1) misunderstanding the data presented, 2) using incomparable definitions, and 3) by deliberately misinterpreting the data presented, especially if it is a biased representation. An example of misunderstanding the data presented could be someone that is researching crime statistics in an area where they are looking to relocate and raise a family; if they do not understand the manner in which the data is being presented, it could be more than easy for them to misunderstand it, this can even apply to the person who wrote the article or table on the data, unknowingly in this situation. An example of using incomparable definitions typically occurs when data is compared from two different sources. Global warming is a topic that many different scientists have researched and published their data, so if someone presenting this data used two different sources, the definitions and sample characteristics might not be the same, essentially making the data invalid due to its misrepresentation. Lastly, an example of deliberately misrepresenting data could be when politicians intentionally manipulate charts, graphs, and articles due to their bias to win the election.
References
Grove, S. K. (2007). Statistics for health care research: A practical workbook. Edinburgh: Elsevier Saunders