What are the characteristics of a population for which it would be appropriate to use mean/median/mode? When would the characteristics of a population make them inappropriate to use?

Topic 1 DQ 1

How can graphics and/or statistics be used to misrepresent data? Where have you seen this done?

Respond to discussion 1

Misrepresenting data can occur with graphs and or statistics by excluding certain demographics, treatments, groups and or cultures that do not corroborate the cause or benefit the target population. For example, according to Dorian and Guirardello, while a survey conducted on nurses’ probability to continue in their current practice included an array of specialties associated with the profession, it lacked the capability to verify email addresses, thereby causing concern for accuracy in response calculations. Furthermore, it is speculated that nurses may have overlooked the message, or the emails were dated. In addition to the professionals’ research participation culture, culminated in a large number of incomplete data (n = 488), which were excluded from the analysis. (Dorigan and Brito Guirardello, 2017)

The only thing that comes to mind is the census. When the surveys are conducted there are some people who decline to participate however, population statistics are recorded related to the available data compiled. Historically, the census has missed large numbers of people in poverty and racial and ethnic minorities. (Bass and Meier, 2015) Ultimately, this misrepresentation affects federal assistance for those groups.

Works Cited

Gary Bass and Adrain Meier. (2015, November 05). An Insidious Way to Underrepresent Minorities. Retrieved from The American Prospect: http://prospect.org/article/insidious-way-underrep…

GIselle Hespanhol Dorigan and Edinêis de Brito Guirardello. (2017). Nursing practice environment, satisfaction. Acta Paulista de Enfermagem, 129 – 135.

Respond to discussion 2

Graphics and statistics are used for giving an idea regarding what the outcomes can be, based on the historical trends and various other factors. It provides us with a measure of a probability of viewing a certain outcome. Statistics can be easily misused while analyzing data, be it accidentally or intentionally. Various variables are often ignored by the one who is analyzing the data. Apart from all this, there are various assumptions made, which could either be false or true. These assumptions don’t provide a clear picture. Without any solid grounds, the person analyzing the data can come up with suggestions and recommendations that are not based on the true findings.

The data source, if not factual, can simply reflect a biased, misleading statistic based on information which is not true. This false information can lead to a false publication. Certain data has outliers which are included by the researcher. These outliers can lead the researcher in generating results which are not accurate, thus misguiding the readers. This can be seen done in various research institutes, which involve thesis and research publications.

Respond to discussion 3

Graphics and/or statistic can be used to misrepresent data in various ways. The information can be misused, misinterpreted, and biased. It can be very easy to manipulate the statistical information and hide data, projecting only what you want the viewers to see. One of the biggest problems that arise causing statistical error is an inadequacy in sampling size, or a bias in the sampling size (De Smith, 2015).

Or simply, sometimes graphs are shown which do not start at zero or have an incorrect line placement.

A good example is a graph regarding Planned Parenthood from 2015. Republicans tried to make it look like money was being misappropriated, where the number of abortions was up and breast cancer exams being performed decreased. Based on the structure of the chart it does look like this is true, but a closer look revealed that the chart had no y-axis, and therefore there was no justifiable placement of the lines. A revision of the chart with the correct y-axis looked much different (Troester, 2016).

De Smith, M. (2015). Statistical Analysis Handbook. Retrieved February 20, 2018, from http://www.statsref.com/HTML/index.html

Troester, H. (2016, August). Misleading Statistics Examples. Retrieved February 20, 2018, from https://www.datapine.com/