Discussion: Reflect on what you learned in this course and the applications of statistical concepts in your personal and professional accounting career.
Discussion: Reflect on what you learned in this course and the applications of statistical concepts in your personal and professional accounting career.
8-1 Discussion: Reflection
One of the most practical applications of learning about statistical analysis is being able to solve real-world personal problems that one encounters in their own life. For example, one can use statistics to help them decide whether or not to go on a diet, how much money to save for retirement, or what type of health insurance plan to buy. In addition, statistical analysis can also help an individual make better business decisions (Lnenicka et al., 2020). For instance, in my case, I always use statistical knowledge to forecast how well my business might do over time and when considering expanding my business venture to determine whether or not the potential profits from the expansion outweigh the costs.
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Data analysis is constantly evolving as we learn more about how to collect and interpret data. In the past, data was often collected manually and then analyzed using simple statistical methods. Today, we have much more sophisticated methods for collecting and analyzing data, and workplaces are increasingly relying on data analysts to help make decisions. Data analysis is changing the way we work in a number of ways (Signoretti et al., 2021). First, we now have access to vast amounts of data that can be used to inform decision-making. This data can come from a variety of sources, including social media, sensors, and transaction records. Second, we have better tools for analyzing this data than ever before. We can use machine learning algorithms to find patterns in different processes and activities.
Statistics can often be persuasive and misleading due to the way they are presented or interpreted. For example, when one is trying to argue that a new product is popular among young adults. They could present statistics that show the product is being purchased more and more by adults aged 18-24. However, what those statistics do not show is whether or not those adults actually like or use the product – they could just be buying it because it is trendy. In this instance, the statistics are persuasive because they show an uptick in popularity, but they are also misleading because they do not give the whole story.
References
Lnenicka, M., Kopackova, H., Machova, R., & Komarkova, J. (2020). Big and open linked data analytics: a study on changing roles and skills in the higher educational process. International Journal of Educational Technology in Higher Education, 17(1), 1-30. https://educationaltechnologyjournal.springeropen.com/articles/10.1186/s41239-020-00208-z
Signoretti, G., Silva, M., Andrade, P., Silva, I., Sisinni, E., & Ferrari, P. (2021). An evolving tinyml compression algorithm for iot environments based on data eccentricity. Sensors, 21(12), 4153. https://doi.org/10.3390/s21124153
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Short reply to this discussion.
8-1 Discussion: Reflection
I this course, you learned core applications of statistical analysis to solve real-world personal or professional inquiry problems (Accountant). You also learned different techniques to draw conclusions from data. These experiences allowed you to practice designing an approach to a statistical problem, considering assumptions and constraints, and developing interpretations and conclusions. Think about how you felt when you first started the course and how you feel now. Reflect on what you learned in this course and the applications of statistical concepts in your personal and professional accounting career.
In your initial discussion post, specifically address the following:
What are some practical uses in your own life for the skills you gained in this class?
How is data analysis changing in the world around you, including workplaces?
How can statistics be persuasive and misleading? Please provide an example.