Please Respond to these discussions APA format with a reference
Statistics are commonly used to prove or disprove theories related to illness and medications. Even if the study is carefully planned to conduct as a result of applications with errors, the misleading results might be obtained (Eracan, al et., 2007). Data may be misrepresented based on a companyâ€™s or individualâ€™s biased opinion, especially when the individual or company has a personal, or monetary, interest in a cause. Data may be misrepresented due to the sample population used, if they need to meet a specific criteria or random selection without background knowledge. Another problem is related to the people in charge of the surveys. Often these people do not have a solid understanding of the subject at hand. If the surveyor doesnâ€™t understand what they are working on, then itâ€™s hard to be certain they are collecting and interpreting all of the data properly. The interviewer may also manipulate the data by manipulating the test subject, to lead the subject to a certain conclusion. This may be accomplished by changing the way they form the question, or the way they ask questions (Lenyo, n.d.).
In the past I have seen data related to the unintentionally represented, related to the route of delivery, and the reason why the route was used (cesarean, vacuum, or forceps births). If the documentation isnâ€™t completed properly then the manager may interpret the information wrong. For example, it is documented that a patient had a vacuum delivery, but it actually was a forceps delivery, or the vacuum was placed on the infantâ€™s head but no suction was applied, & therefore the delivery was not vacuum assisted, but was documented that it was placed.
Ercan, I., YazÄ±cÄ±, B., Yang, Y., Ã–zkaya, G., Cangur, S., Ediz, B., & Kan, I. (2007). Misusage of Statistics in Medical Research.Eur J Gen Med, 4(3) p128-134. Retrieved from: http://www.bioline.org.br/pdf?gm07030.
Lenyo, M. (n.d.). Crooked Numbers Using Opinions to Shape Statistics. Ethica Publishing. Retrieved from: http://www.ethicapublishing.com/6CH3.htm.
A working understanding of the major fundamentals of statistical analysis is required to incorporate the findings of empirical research into nursing practice. Graphics and statistics are used as a means of interpreting qualitative and quantitative data in terms of different variables and their relationships (Grove & Cipher, 2017). Nursing knowledge based on empirical research plays a fundamental role in the development of evidence-based nursing practices. The ability to interpret and use quantitative findings from nursing research is an essential skill for advanced practice nurses to ensure provision of the best care possible for our patients (Stommel & Dontje, 2014). Graphics and statistics are used to represent data, how it is interpreted, and used depends on the creator and determines whether or not the data has been misrepresented. Data misrepresentation using graphics and/or statistics is seen daily in false advertisements.
In advertising, there’s a big difference between pushing the truth and making false claims. Is a product really scientifically proven? Are the results truly results guaranteed? One such scandal is the Kellogg’s popular Rice Krispies cereal crisis in 2010. The Kelloggs company was accused of misleading consumers about its immunity boosting properties. The Federal Trade Commission ordered Kellogg to halt all advertising that claimed that the cereal improved a child’s immunity with “25 percent Daily Value of Antioxidants and Nutrients — Vitamins A, B, C and E,” stating the claims were “dubious.” (Rafael & Ignacio, 2014). As it pertains to us, in nursing data misrepresentation can be seen in false evidence-based practice claims and/or false documentation.
Grove, S. K., Cipher, D. J. (2017). Statistics for Nursing Research: A Workbook for Evidence-Based Practice, 2nd Edition. Retrieved from https://pageburstls.elsevier.com/#/books/978032335…
Rafael Di, T., & Ignacio, F. (2014). Government Advertising and Media Coverage of Corruption Scandals. American Economic Journal: Applied Economics, 2(4), 119-131.
Stommel, M., & Dontje, K. J. (2014). Statistics for Nurses and Health Professionals. New York, NY: Springer Publishing Company.
Statistics are a large part of medicine and documenting and exploring new findings in the medical field. However, when data is not portrayed properly with the use of statistics, it cost time and money and also poses a risk to science and humankind (Ercan et al., 2007). Even when studies and analysis are done meticulously, one small error can result in a larger and more cumulative error. It is believed that over 50% of medical writings and research have some sort of statistical error (Ercan et al., 2007). The main reason for these mistakes is a lack of accurate statistical knowledge and a failure to work with a statistician when doing these research studies (Ercan et al., 2007).
Marketing/retail and the media are two areas that misrepresentation of statistics occurs, in my opinion. Often times the media will only share the part of the statistical finding that will cause uproar or will tug at your heart strings. They donâ€™t share the big picture and all the facts. Only some percentages or only part of the graph is shown or is shown with a bias. In marketing and retail, prices may be changed based on limited data. There is a larger sample size and variance that takes place in retail especially that effects sales and prices and retailers fail to see this (Wharton University of Pennsylvania, 2008). In marketing, only the parts or features of the product are appealing will be shared with you. For instance, letâ€™s say there is a new probiotic on the market. The company may say that 98% of consumers reported an improvement in their abdominal bloating. That statistic is great and would make anyone want to buy it and may even be true. However, what the company failed to share is that only 98% of one of their sample studies reported improvement, not 98% of the total of all sample studies. Sharing one part of a data finding is a big way that statistics are misrepresented.
Ercan, I., YazÄ±cÄ±, B., Yang, Y., Ã–zkaya, G., Cangur, S., Ediz, B., & Kan, I. (2007). Misusage Of Statistics In Medical Research. European Journal of General Medicine, 4(3), 128-134.
Wharton University of Pennsylvania North America. (2008, April 02). The Use – and Misuse – of Statistics: How and Why Numbers Are So Easily Manipulated. Retrieved from http://knowledge.wharton.upenn.edu/article/the-use…
Graphics or statistics can be used to misrepresent data in many ways. Questions can be asked in a way to lean towards the results that are desired by the polling team, which would not fairly report the results of both sides. Inadequate studies may be used not including everything necessary to analyze the data correctly. Inadequate planning before the study can affect the outcome and misrepresent the data obtained. If the knowledge level of those making the questions and reporting the data is inadequate the results may not be accurate. Sometimes data does not compare apples to apples, the data may have been collected differently and so when the results would come out the data will not be correct.
Car sales are a good example of data misrepresented. Each car company always says that they are the number one! Obviously, they all cannot be the number one, but according to commercials they are. The number one in last 10 years and still on the road, the number one in satisfaction, the number one in bestselling. Also, number one in crash testing, unless all the crash testing is done at all the same speed with all the exact same factors would it then be an accurate representation.
Thiese, M. S., Arnold, Z. C., & Walker, S. D. (2015, February 15). The misuse and abuse of statistics in biomedical research. Retrieved May 1, 2018, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC44013…
Statistics Canada. (2011, June 14). Common menu bar links. Retrieved May 1, 2018, from https://www.statcan.gc.ca/edu/power-pouvoir/ch6/mi…
How can graphics and/or statistics be used to misrepresent data?
There are many ways graphics and statistics can be misrepresented in terms with data. â€œStatistics is a collection of methods for planning experiments,obtaining data and organizing, summarizing, analyzing, interpreting, presenting and drawing conclusion based on the dataâ€ (Grand Canyon University, 2013). Having any personal opinion or also known as bias opinion can affect the end results. It is important to steer clear of any unethical behavior to help keep the focus away from hiding certain data or not having the correct numbers published that may correlate with the data collected (Ethics in Statistics, 2013). Graphics and statistics are also used as a persuasive tool to persuade people to gain popular opinions. This is seen more in media, politics and businesses.
Where have you seen this done?
Most crimes statistic that has ever been reported in the news is more than likely misleading. A lot crimes have low incidence of occurrence, which makes the variation as a percentage very high. This is often used to exaggerate news to scare people into spending money where it is not needed. For instance, murders will be rare in smaller community. So, if 1 happened the first year and then 2 happened the next, the murder rate will be likely to reported to have increased by 100%. It is true if it happens the opposite way. If the murder number went down from 2 to 1, then that means that the rate was cut by half. This can be called sensationalized crime statistics, which is used to attract viewers to the news and fund programs that possibly may not be helpful to the larger population or to justify the need and continuation of spending that may not be effective. .
C. (2016, January 21). How To Make Sense Of Conflicting, Confusing And Misleading Crime
Statistics. Retrieved April 30, 2018, from
Ethics in Statistics. (n.d.). Retrieved April 30, 2018, from Explorable.com
Statistics or graphics can be used to misrepresent data when the source are not reliable. Data can be misguiding if the sample is not done properly.
Statistics or graphics are used for the purpose of approve, disapprove therories, analysis and interpretation of studies. Data can be misrepresented if there is a special interest or bias. individual opinions without consider the general aspect can affect the interpretations of data. Reliability examines the amount of measurement error in an instrument that is used in a study. Random selection without background standard criteria leads to misinterpretation of data.
Data can be bias in a competitive market. for instance in pharmaceutical companies. Pharmaceutical compainies use some misguided data to sell thier product. Using generic terms for the same product. the action and purpose of the medicine will be the same but using branded name to advertise. this may lead to misrepresention of data and misinterpretation.
Grove. S.K Cipher , D.J Statistics for Nursing Research; A workshop for Evidence-Based Practice. 2nd edition. (Elsevier)