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:
What are some practical uses in your own life for the skills you gained in this class?
The technologies that push many of the products we buy watches, and gadgets we use today were invented, developed, and perfected through the use and efforts of demographers, mathematicians, and statisticians. Statistic application is achieved on a large scale with massive budgets and austere devotion to scientific methods applicable across all industries, government organizations, education administration and institutions, business leaders, marketing, geology, medical organization, and scientific researchers. They all use statistical methods and processes during their work. However, on a personal level, the application of it to my real world with the knowledge I have acquired from the class has changed the way I do and program my day-to-day activities, from the way I choose which routes to use from one point to the other either from work o home and vice versa, how much to charge for a service or product that I have produced, how long a specific activity will take, or how much to pay for a service or a product online to planning my bills and trajectories of my income and expenditure (OpenStax. n.d.).
How is data analysis changing in the world around you, including workplaces?
Data analysis is becoming the norm in today’s drastically changing world, from technology and political economics to our social way of life. Its positive effects are felt everywhere, from communication and connecting people and societies to new ways of managing, measuring, and controlling companies and businesses. There is also a profound way of searching with storage of information, analyzed data from research digitized journals and books. All these data activities have curated our tastes and preferences to what we need with customized information from all cultural tastes. These have made life more accessible, and data analysis has saved lives from medical research, hospital operations, and patient outcomes. The business world has been simplified with data analysis; all around the world, businesses rely on data, with each company having its pool of data sets for its customers that assist in customer demographic information, to product usage data that informs UI decisions making to the development of new features for the services and products they produce. Data analysis has also improved productivity in all sectors of the economy and world governance and policymaking. And all this information analyzed data and be stored effectively and efficiently manually in servers or the cloud for future reference and use; the possibilities of data analysis are limitless in their merits to our daily lives and the way we work in our day-to-day living from our homes to work stations (Thornham, 2023)
How can statistics be persuasive and misleading? Please provide an example.
According to Donohue, (n.d.), the limitations of statistics are more than what we can achieve if the information analyzed is used in the wrong manner or it was incorrectly arrived at with the wrong ways of collecting, analyzing, and the conclusion that was deducted from the analysis. Due to carelessness or if the data needs to be understood correctly, there are mistakes in the interpretation leading to misleading statistics. A good example is that statistics is always quantitative. It negates the qualitative nature of life and its phenomenon, which cannot be expressed in quantitative forms. It also does not deal with individual items; it is an aggregate means of facts; as such, it does not show the entire story of happenings and is liable to be miscued as its laws are not exact. They rely on possibilities of occurrences but not actual events. Statistics are not always beyond doubt as assumptions are always there for each study, with many methods employed to study a problem (Mehta, 2015).
I take this opportunity to wish all my classmates much success in all their upcoming classes and future endeavors’. Instructor Matthew, thank you for all your support in this class, It is very much appreciated. Your videos and course materials were very helpful and I can say proudly I have completed this statistics class because of all the support you provided, guidance and encouragement and I enjoyed being your student. I Wish you the very best in your teaching career.
References
definitions of Statistics, probability, and key terms – introductory statistics. OpenStax. (n.d.). https://openstax.org/books/introductory-statistics/pages/1-1-definitions-of-statistics-probability-and-key-terms
Donohue, J. (n.d.). The upside of Data. Jessica Donohue: The upside of data | TED Talk. https://www.ted.com/talks/jessica_donohue_the_upside_of_data?utm_campaign=tedspread&utm_medium=referral&utm_source=tedcomshare
Mehta, P. (2015, August 12). 8 main limitations of statistics – explained! Economics Discussion. https://www.economicsdiscussion.net/statistics/8-main-limitations-of-statistics-explained/2321
Thornham, P. (2023, March 9). Nathan Tintle Presents: Introduction to statistical investigations now with Zybooks! zyBooks. Retrieved April 26, 2023, from https://www.zybooks.com/nathan-tintle-presents-introduction-to-statistical-investigations-now-with-zybooks/
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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