Introduction
Parametric and nonparametric statistical methods play an important role in career development. Biostatistical analysis involves the determination of public health information in terms of accuracy. Gathering information forms a large portion of this field and necessitates a proper understanding of the appropriate tools to use when making such decisions. Nonparametric methods elicit a lot of promise in the public health sector due to their overarching nature when making assumptions about the population. Many public health studies involve a varied population and depend on distribution-free methods. Nonparametric designs are helpful in real-world situations as they do not evidence fixed parameters (Sullivan, 2018). They do not depend on a particular population as in cases involving parametric methods and are relatively easy to understand and apply. Such an accurate depiction of statistics would help propagate an individual in the workforce as it provides a wide range of possibilities for an illness to mutate. Mitigating the limiting effects of constraints also promotes nonparametric methods as researchers can use them in varying situations.
It is important to analyze the necessity for nonparametric statistical methods because of their application with data that can be ranked. Many groups of people who contract an illness may fall into a specific category explaining the illness’s origin. For instance, people who contract Ebola have come into close contact with someone consuming a sick monkey. Individuals are unlikely to exhibit symptoms of an illness if they do not have any link to its spreading mechanism. This style is helpful in the real world as it provides a lot of information on any disease’s transmission and spreading capacity. The unit also provides me with information regarding the methods’ interpretations or underpinnings. Individuals are more inclined to use nonparametric methods as they have fewer assumptions than parametric designs (Sullivan, 2018). They mainly utilize ordinary data and promote popular statistical methods such as using a Chi-square method.
Furthermore, it is important to determine the efficacy of nonparametric methods as ideal for use because they are mainly unaffected by outliers. While some statistical methods would crumble under this variation, nonparametric methods do not consider values that deviate from the mean (Sullivan, 2018). Nonetheless, it is important to consider the importance of such data, though most do not elicit a large difference from the mean or standard deviation. As a result, these statistics methods do not necessitate researchers to use other analyses to propagate parametric methods, illustrating their focus on simplicity (Sullivan, 2018). However, this simplicity may serve as a caution in some instances as I have chosen parametric methods over nonparametric designs on various occasions based on the complexity of information to be analyzed.
Parametric and Nonparametric Statistical Methods
Nonparametric statistics introduced in this unit have enabled me to discern their use on ordinary or nominal data. The previously mentioned test, the Chi-square, and any modification exhibiting its characteristics are utilized for nominal data. This statistical method is uniquely suited to nonparametric designs as they are primarily suited to understand data measured using an ordinal measurement scale.
Working on this unit is also positively inclined to a real-life situation as I can represent information gained through data visualization techniques. This graphical representation technique is important to provide an accurate and simple communication method to one’s audience. I have learned to develop clearly labeled bar graphs, lines, and charts to inform an audience in a stimulating way effectively (Tandon, 2017). Using this method is helpful to information presentation as they capture the audience’s attention. These individuals are more likely to portray interest and thought on the subject than their counterparts with no exposure to graphical representations.
I have also developed a better capacity to represent information visually using texts, figures, and tables. In the first instance, I use a few numbers and illustrate ancillary data to the main analysis. The unit promotes the use of tables to represent main findings as a means of helping readers discern the content of a text without reading it (Tandon, 2017). Figures help define complex relationships while indicating any trend that may be elicited over time. This unit also showcases the variations presented based on one’s geographical location, items that would appear in a figure while also illustrating the main findings, as is the case with tables.
Conclusion
In conclusion, this unit is helpful for public health studies due to the introduction of nonparametric statistical methods. While parametric methods are useful for normal data, nonparametric designs do not require such a trend. Medical phenomena are unlikely to follow a pattern and may elicit various outliers that would adversely affect the analyses of any qualified researcher. However, using nonparametric methods alleviates this notion and provides accurate information using easy formulas that are relatively simple to follow. The unit combines nonparametric tools with data visualization, promoting memory development as individuals are likely to remember bars or charts rather than texts. These designs also help in quick analyses of information and help researchers accurately assess a phenomenon without reading the text. Therefore, unit eight forms a major component in this course by providing me with measures to use when analyzing information for real-life organizations in the future.
References
Sullivan, L. M. (2018). Essentials of biostatistics in public health (3rd ed.). Jones & Bartlett Learning.
Tandon, D. (2017). The Importance of data visualization in your business and 10 ways to pull it off easily. The Kini Group. Web.