Two years ago, while I was taking a much-needed break from the COVID influenced
restaurant industry, I finally took action on something I had been wanting to do for a long time;
I wrote a book about personal finance, debatably an odd passion, but a passion none the less.
For our first class discussion assignment, when prompted to think of a topic I was passionate
about, I landed on the question, “Why isn’t personal finance taught in schools?”. I have secretly
always wanted to tour the country as a speaker/teacher of personal finance, making stops at
high schools, universities, and corporations to talk about the importance of saving and knowing
how money works in the “system”. Topics of discussion would include credit card usage,
mortgages, the stock market, insurance, etc. I believe that young adults should receive formal
education about these subjects rather than relying solely on their parents, friends, and the
internet. Debt is a serious problem in our country and a lot of people do not even know how
they got into debt and believe deep down that they’ll never get out. If they were just
knowledgeable about simple finance, I’d bet they would have avoided debt all together in the
first place.
The internet article I have chosen to review can be found here. In just the first
paragraph of the article you can find an analytical context change. Found in Chapter 2 of Essays
on Data Analysis, “The context of a problem covers many factors, including the question that
gave rise to the dataset and the motivations of the people asking the questions.” The article
originally asks my question of “Why isn’t personal finance taught in schools?”; however, instead
of pointing fingers and blaming schools, the article wishes that action be taken and alters the
question to read “How do we get schools to teach personal finance?” The article, like me, is
concerned with the illiteracy of most people towards personal finance. The article quickly dives
into some numbers and cites the National Financial Educators Council in saying, “Financial
education programs benefit 100% of students. Compare this to only 19% need high school level
algebra in their job.” As well as citing the National Bureau for Economic Research when adding,
“Low-income workers attending an employer-sponsored financial education program were
11.5% more likely to participate in 401(k) plans and save more for retirement than peers who
elected not to attend the education initiative.” The article made me change my original
question to something more proactive and positive; it goes on to give statistics about kids
emulating their parent’s finance habits as well as young adults being in debt.
After reading Chapter 2 of The Elements of Data Analytic Style, I conclude that the
article I have chosen would best fit within the analysis categories of exploratory and predictive.
There is certainly descriptive analysis in the way of statistics, but the article does move further
by “searching for correlations or relationships” (p.10). When personal finance is taught to
someone, that someone is more likely to behave financially responsible. I added predictive to
my category choice because “in some cases, the set of measurements used to predict the
outcome will be intuitive” (p.11). The article is predicting that if we taught personal finance in
schools, then everyone would make better financial decisions, limiting unnecessary debt and
increasing retirement savings. As we continue on in Chapter 2 under the 2.8 heading of
Common Mistakes, we can see that I could be “overfitting”, “interpreting an exploratory
analysis as predictive” (p. 13). It seems to me that individually, the given data points in the
article are each exploratory, but as a whole they are predictive.
I briefly touched on context earlier and in this particular case, knowing the context
would be very important if you were to try and make a pitch to school boards about adding
some kind of personal finance 101 class to the curriculum. In Essays on Data Analysis,
motivation of the question is important and, in our case, here, the motivation is positive and
obvious. Switching gears to the resources of the data analysis, “the most critical resource for a
data analyst is time” (p. 15). Some of the data referenced in the article, which was written in
2022, was cited as far back as 2004, so it feels safe to say that the data checks the box of time
elapsed to make conclusions. There is without a doubt more analysis out there on the subject
matter, but the article takes from multiple sources over multiple decades and has a complete
thought finalized in the given article. “Who is the data for?” (p. 15) The audience here would be
school board members, teachers, parents, students, education council, lawmakers, and the like.
With so many audiences, the article does a great job of giving data that is relevant and
understandable to each audience group while delivering a sound summary and an impactful
message.
“The goal of data analysis is to produce something useful” (p. 17). This article has
achieved that goal. With the original question of “Why isn’t personal finance taught in
schools?” the article made a savvy data analysis examination and recognized the real question
that needed to be answered was “How do we get schools to teach personal finance?” Essays on
Data Analysis dives deeper into this idea of “Questioning the Question” in Chapter 3 where we
learn that the initial question does not need to be taken as scripture, but as a data analyst, one
must dance around the initial question by asking other questions and finding out more
information to ultimately arrive at the correct question that needs to be analyzed. Before
“engineering the solution”, a data analyst must go through rigorous and tedious work to ensure
the correct question is being asked. The article provoked many ideas in my head and maybe
there is a volume 2 of my book in the future.