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Statisticsweek1DQresponses.docxDominique Lazo-Johnson
WednesdaySep 16 at 8:45pm
Quantitative analysis guides rational thinking. In the data-driven world we inhabit today, a foundation of quantitative analysis allows us to throw away the notion of a hunch or simple qualitative observations of data. As datasets stabilize, they tend to become predictors of future outcomes. Quantitative analysis presents the opportunity to pseudo-view the future in a sense. When we look at economic applications of statistics and data analysis, so much of it is grounded in going against fallacies such as the gambler’s fallacy. Far too often, we believe that data is fatalistic and will correct itself in some period of time. “While you are learning about how to organize, summarize, and interpret data using sta-tistics, it is also important to understand statistics so that you can be an intelligent con-sumer of information (Lind, 2017).”
Businesses and individuals rely heavily on methods of quantitative analysis today. Netflix and Amazon know our TV viewing habits and the items we will tend to order every holiday season as billions of hours are logged in sorting through catalogs of shows and products. Retail investors use data trends and great sample sizes to identify wise investments. To not enable ourselves to take advantage of data and quantitative analysis at our disposal would be irrational if you are aware that your neighbors and friends are doing exactly so. We rely so much on quantitative and statistical models that we tend to have difficulty accepting word-of-mouth as fact or rational today. Just as businesses and individuals yearn for efficiency, data and methods of analysis fulfills those desires.
I was drawn to the following statement by Joseph Stalin, the famed 20th century communist leader of the Soviet Union, “A single death is a tragedy; a million deaths is a statistic.” I immediately thought of the COVID-19 pandemic and the data presented by case and fatality figures and the pertinence today. If we consider a single loss of life, especially one dear to us, we may resonate more with the aspects of tragedy, whereas the cumulative stock of fatalities becomes a venue for statistical analysis and identifying trends.
References:
Quotes retrieved from www.goodreads.com/quotes/search?utf8=%E2%9C%93&q=statistics&commit=Search (Links to an external site.)
Lind, D. A., Marchal, W. G., & Wathen, S. A. (2017). Statistical techniques in business and economics. (17th ed.). Boston. McGraw-Hill/Irwin
ThursdaySep 17 at 11:27am
Quantitative analysis is one of the most crucial parts for identify and exploring a dataset. The use of statistical mathematical methods to exploit a dataset and get a fundamental understanding of what is inside the dataset. Data is growing at an alarming rate, making traditional methods of looking at datasets not valid. Lind and Marchal pointed out, " As people use Google to search the Internet, Google records every search and then uses these data to sort and prioritize the results for future Internet searches. One recent estimate indicates that Google processes 20,000 terabytes of information per day" (Chapter 1). At that scale of data quantitative analysis is crucial for being able to understand the dataset at a high and low level view.
Another aspect to look at this topic, is the world of machine learning and artificial intelligence is growing. This field is becoming a key part of every business. I currently work in this field both technical as a machine learning engineer, and as manager of a team data scientists and engineers. At the very core of our work is exploiting datasets using quantitative methods. Without these methods our job could not be completed. It is at the core, gives us the ability to understand relations within the data to understand how to build new machine learning algorithms to be used with the data.
Reference:
Lind, D. A., Marchal, W. G., & Wathen, S. A. (2017). Statistical techniques in business and economics (17th ed.). Retrieved from http://connect.mheducation.com/class/
WednesdaySep 16 at 9:46pm
I reviewed a study researching stigma around COVID-19 across two time periods. The first time period refers to the period prior to March 16, 2020. The second period refers to the period after March 16th. This date was specifically chosen as it was when the President of the United States first used the terminology to describe the virus. The goal of the study is to understand negative stigma surround the reference to COVID-19 as the “Chinese virus”. Interestingly, the researchers used data from Twitter to analyze the mentions of “Chinese virus” in users’ tweets. The findings did conclude statistically significant increases in the use of the stigma-driving term. Most notably, there is reason to believe that stigma can be cultivated and disseminated through social media platforms. There was an average increase of 997% regarding tweets including the term, with a minimum of 661% and a maximum of 1447%. “All 50 US states witnessed an increase in the number of tweets exclusively mentioning “Chinese virus” or “China virus” rather than COVID-19 or coronavirus. The 5 US states with the highest number of postperiod “Chinese virus” tweets were Pennsylvania, New York, Florida, Texas, and California (Budhwani, 2020).” Their findings suggest societal impacts due to the stigmatizing language that is perpetuated on the Twitter platform. “If these stigmatizing terms persist as malicious synonyms for the novel coronavirus, reparative efforts may be required to restore trust by marginalized communities (Budhwani, 2020).”
References:
Budhwani H, Sun R
Creating COVID-19 Stigma by Referencing the Novel Coronavirus as the “Chinese virus” on Twitter: Quantitative Analysis of Social Media Data
J Med Internet Res 2020;22(5):e19301
ThursdaySep 17 at 5:55pm
I research how quantitative analysis is used by physicians in practice with artificial intelligence. The researchers were able to collect surveys and compile the data into excel sheets. Once this was done it allow them to be able to use a vast amount of data exploitation to compile their results and give meaning to them. A mean issue when working with data is bias in the data. Petkus et al. stated "In addition, as well as capturing quantitative survey data, we took account of their responses to open questions (qualitative data) and analysed this in duplicate to reduce confirmation bias" (p. 327). Using quantitative analysis methods help reduce and understand the bias in their data allowing them to make more accurate claims in their results.
This is an import strength to suing statistical based methods when exploring data. Especially in world of AI where bias in the data can be the difference to between a well trained model and a terrible one. If the bias is not understood well, it can lead to an overfitted model that will break when presented data outside of that bias. It is imported to use quantitative methods to explore and understand your data right at the start to make sure that research moving forward is well understood.
Reference:
Petkus, H., Hoogewerf, J., & Wyatt, J. C. (2020). What do senior physicians think about AI and clinical decision support systems: Quantitative and qualitative analysis of data from specialty societies. Clinical Medicine, 20(3), 324–328. https://doi-org.proxy-library.ashford.edu/10.7861/clinmed.2019-0317
