This week Courtney and I looked back on our articles that
focused on the Probability Unit in Statistics. The three vocabulary words we decided
to focus on were bias, probability (with independence), and sample size. Each word
was highlighted in a different article and encapsulates part of the theme for
each article.
In the article “Wait, Have We Really Wiped Out 60% of Animals?”
by Ed Yong, we associated the word bias. We chose the vocabulary word bias
because there are many sources out there today that include an immense amount
of bias and sometimes it Is very hard to see it at first. This article shows a
great deal of bias by explaining the impossibility of humans wiping out 60% of
animal species in a total of about 49 years according to the World Wildlife
Foundation. Not all animal populations were accounted for in their study, which
is a prime example of bias.
In the article “No, CBS Sports, The Patriots Have Not Found
an Edge on Coin Flips” by Harrison Chase, this article give explanation and
calculations to the independent events of multiple coin flips. We chose the
word/s independent probability because coin flipping is one of the main
examples that are used in the Probability Unit to demonstrate independent
probabilities. Independent probability is basically when two or more events can
occur with having no effect on the next. So whether the Patriots flip a coin
once or 150 times, their probability every time of getting a heads or tails to win
their coin toss is still going to be 50% each time.
In the article “Moneyball Shows the Power of Statistics” by
Kevin Rudy, the word/s that we chose was sample size. We chose this because it demonstrates
some of the consequences of having a small sample size. Billy Beane of the Oakland
A’s claimed that because the sample size was small, that was why his statistical
analysis of finding undervalued players that could make up for overvalued
players (for teams to save money on payroll) was not proof that his system didn’t
work. Having a good sample size when trying to represent a whole population is
important. Because Billy Beane did not have enough in his sample to make his
metrics true across the board, his system did not seem to work to those that
were critics of him.
In teaching these important vocabulary words to our students,
Courtney and I came up with using the Semantic Question Map and the Frayer
Model. “The Semantic Question Map is a variation on the Semantic Map, but its
general design is fixed. The focus word is placed in an oval in the center of
the map, and then several questions about it are raised. The questions, which
may be provided by the teacher or generated by the students, are placed inside
the ovals that extend from the oval containing the focus word” (McLaughlin,
2015). When specific word or topic is to be highlighted in a class, this map is
to help students focus on that aspect. The Frayer Model allows students to focus
on vocabulary. The center oval contains the word of focus and surrounded by
four boxes, the word’s definition, characteristics, examples, and non-examples
are further explained (E^2 Math, 2013).
I decided to use the Semantic Question Map to focus on the
word/ topic bias. I chose to use this graphic organizer to teach the “Wait,
Have We Really Wiped Out 60% of Animals?’ because it can help students focus on
the main aspects surrounding bias, such as the definition, examples,
consequences, and how to eliminate or create an unbiased sample. In my model, I
go about focusing on those aspects by asking them in a question form. I provide
acceptable answers in each connecting box in red based on the notes from the
lesson that I had taught. This will help to keep students organized and have a
resource when they go to create their own survey or experiment.
References
McLaughlin, M. (2015). Content Area Reading: Teaching and
Learning for College and Career Readiness. Pearson Education.
E.^2 Math (2013-2014). Read Like a Mathematician. Arizona
Common Core State Standards. Retrieved from
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