1. Do you look at statistics differently now? If so, how?
2. What advice can you offer to help others make the most appropriate use of data?
THIS IS WHAT I SAID FOR MY DISCUSSION POST SO YOU CAN USE THAT:
The skills I gained in this course have practical applications in my personal life. Statistical analysis techniques can be useful in analyzing personal finance data (Mölder et al., 2021). I can examine my spending patterns, budgeting habits, and investment decisions by applying statistical methods. For example, I can use regression analysis to identify correlations between my income and expenses, helping me make informed financial decisions.
Another practical use of statistical analysis in my personal life is in health and fitness monitoring. By collecting data on my physical activity, diet, and health indicators, I can use statistical techniques to track and interpret the information. For instance, I can use time series analysis to identify trends or patterns in my fitness progress. This can help me adjust my exercise routine or dietary choices based on evidence-backed insights.
Moreover, statistical concepts like probability can play a significant role in decision-making. When faced with various options or uncertainties, I can utilize probability theory to assess the likelihood of different outcomes (Mölder et al., 2021). By considering probabilities and potential outcomes, I can make more informed decisions. For instance, I can use decision trees or Monte Carlo simulations to evaluate different choices’ potential risks and rewards.
Changing Data Analysis in Workplaces
In the world of workplaces, data analysis is undergoing significant transformations. One notable trend is the emergence of big data. With the rapid growth of data from various sources such as social media, sensors, and online platforms, organizations now have access to vast amounts of information. However, the challenge lies in extracting meaningful insights from this data. Advanced analytics techniques, including machine learning and artificial intelligence, are being leveraged to process and analyze these massive datasets. These techniques help uncover patterns, trends, and correlations that can provide valuable insights for decision-making.
Automation is another crucial aspect of reshaping data analysis in workplaces. With the advancement of technology, many routine data analysis tasks are being automated (Grove & Cipher, 2019). This includes data cleaning, preprocessing, and basic statistical computations. Automation improves efficiency and allows analysts to focus more on interpreting results and making strategic decisions. By automating repetitive tasks, analysts can dedicate their time to higher-level thinking and complex analysis, adding more value to the organization.
Furthermore, organizations are increasingly embracing data-driven decision-making processes. Instead of relying solely on intuition or gut feelings, organizations leverage statistical analysis to drive their decision-making. Statistical analysis helps extract valuable insights from data, identify trends, make predictions, and evaluate the effectiveness of different strategies. By adopting a data-driven approach, organizations can make more informed decisions, optimize processes, and gain a competitive edge in the market.
Statistics as Persuasive and Misleading
Statistics can be persuasive and misleading, depending on how they are presented and interpreted (Grove & Cipher, 2019). For example, take a case of a company that claims their product has a 90% customer satisfaction rate, according to a survey they did. However, a deeper look reveals that the business merely polled a small number of customers who were offered incentives to provide positive feedback. As a result of the skewed sample and the company’s selective data presentation, the statistics in this instance give a deceptively positive impression of the product.
On the other hand, statistics can be persuasive when they are accurately and transparently presented. For instance, a study may demonstrate a statistically significant improvement in patient outcomes for a new medical treatment compared to a control group. In this scenario, the statistics are persuasive because they provide evidence supporting the treatment’s effectiveness. Rigorous analysis, appropriate statistical tests, and a well-designed study methodology strengthen the credibility of the statistics.
Assessing numerous elements to prevent being duped by statistics is essential. Examining the methodology, considering the sample size and representativeness, assessing the data collection methods, and spotting potential biases are all part of this. Examining these elements allows one to judge the authenticity and dependability of the provided statistical data.
References
Grove, S. K., & Cipher, D. J. (2019). Statistics for Nursing Research – E-Book: A Workbook for Evidence-Based Practice. In Google Books. Elsevier Health Sciences. https://books.google.co.ke/books?hl=en&lr=&id=gt3WDwAAQBAJ&oi=fnd&pg=PP1&dq=statistics+in+nursing&ots=thfIXbVsnA&sig=eqD5WwmOx2YBeSfCrWrFfX77pj4&redir_esc=y#v=onepage&q=statistics%20in%20nursing&f=false
Mölder, F., Jablonski, K. P., Letcher, B., Hall, M. B., Tomkins-Tinch, C. H., Sochat, V., Forster, J., Lee, S., Twardziok, S. O., Kanitz, A., Wilm, A., Holtgrewe, M., Rahmann, S., Nahnsen, S., & Köster, J. (2021). Sustainable data analysis with Snakemake. F1000Research, 10, 33. https://doi.org/10.12688/f1000research.29032.2
HERE IS WHAT THE PEER SAID:
Hi Everyone,
This class has definitely been more challenging than other classes I have taken. In those regards though, it has taught me multiple life skills that will lead me to success in my future endeavors. The most beneficial takeaway from this class was becoming proficient in Excel. Before taking this class I did not know how to navigate Excel very well and always struggled with it. Now that we spent so much of our time working in Excel, I can use it in my everyday tasks and work life. This makes me a great asset to the business I work for because I will now be able to create spreadsheets, analyze production, and many more tasks.
Data analysis is changing every day around the world. It has helped change companies for the better. Data analysis allows companies to break down their business and figure out how to make it run more efficiently, watch production, solve issues, and grow as a whole. It shows where some work may be needed in a company and creates room to change whatever is necessary to maximize profits. It can also be used to keep track of employees and analyze who is a good fit for the business and how a team is working together. Technology has come so far now companies can target market their customers and gear their sales toward them.
Statistics can be persuasive and misleading. For example, during a survey, if only a small sample size is used the data can be skewed due to not having a large enough population. A small sample size will only provide answers from a couple of people and lead to false accusations of what an audience actually prefers. Another example of how statistics can be persuasive and misleading is politics. Campaigns can manipulate people to get them on board with their opinion. Statistics can also be interpreted incorrectly causing them to be misleading. It is important to know what to look for in companies and be aware when it comes to advertising and propaganda.
Good luck everyone!
RESOURCES YOU CAN USE TO HELP:
Reading: MAT 240: Applied Statistics, Module 8
In Module 8 of your course textbook, you will explore hypothesis testing for the difference between two population means and proportions. Instead of testing a particular population, you will learn how to establish a comparison between distinct populations. Consider the following questions as you read:
What is needed to construct hypothesis testing to understand the differences between two population means?
Now that you’ve learned about descriptive and inferential statistics, can you find misuses of statistics around you?
Video: https://www.ted.com/talks/jessica_donohue_the_upside_of_data?utm_campaign=tedspread&utm_medium=referral&utm_source=tedcomshare
This video discusses the four lessons learned from working with big data and turning it from information to knowledge. Jessica Donahue, expert in the field of research and data analysis, examines the impact of tools and technology, the chaos associated with large amounts of data and organization schemes, and the processes of developing skills in data analysis and learning how to ask the right questions from data.
A captioned version of this video is available: https://www.youtube.com/watch?v=gnh_yuWL9sc&feature=youtu.be
A video transcript is available: https://learn.snhu.edu/content/enforced/1301995-MAT-240-J5696-OL-TRAD-UG.23EW5/course_documents/MAT%20240%20Transcript%20for%20The%20Upside%20of%20Data.docx?_&d2lSessionVal=sIrsyuJnZcwxj9EtCbMnEXBlF&ou=1301995
Video: https://www.ted.com/talks/mark_liddell_how_statistics_can_be_misleading?utm_campaign=tedspread&utm_medium=referral&utm_source=tedcomshare
This video explores how statistics can be persuasive and the danger of Simpson’s paradox. The same data set may show opposing trends, depending on how it is grouped. Asking deeper questions, exploring different conditions, and looking at data from multiple angles are important techniques to avoid bias.
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