PSY 260 7-2 Discussion
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School
Southern New Hampshire University *
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Course
260
Subject
Medicine
Date
Jan 9, 2024
Type
docx
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Uploaded by KidRoseTurtle8
How do the examples given in the video (jury decisions and medical tests) connect to
what you learned about statistical decision making related to Type I errors (false
positives) and Type II errors (false negatives)? Select either Type I errors or Type II errors
and explain your response.
o
The examples of jury decisions and medical tests given in the provided video are
an excellent representative of how statistics can influence decision making and
coincide with Type I and Type II errors. For the sake of this week’s discussion
topic, I would like to concentrate on Type I errors, or false-positive results, and
the consequences of misinterpreting statistical evidence. When recounting the
potential for a jury to misunderstand statistics, a false-positive could be
detrimental to an individual’s life given the assumption that jury-hearing are
accompanied by the potential for a hefty sentence. This falsehood could
manipulate an individual’s life. Especially when recounting the second example
of a false-positive in the Ted Talk, if a patient receives a false-positive result
regarding a life-threatening illness, that will essentially flip their world upside-
down for no reason. For example, if someone receives a life-threatening false-
positive medical result, they could undergo unnecessary treatment that could have
a variety of side-effects.
Which programmatic theme(s) tie in with the examples?
o
The programmatic themes that tie in with the examples of a false-positive result
occurring in a jury’s decision and a medical test are that of ethics and social
injustice. As mentioned above, both a jury’s decision and a medical test relying on
a false positive would have serious negative consequences. In these specific
examples, two negative consequences include false-imprisonment and
unnecessary medical intervention.
In general, do you think that making Type I or Type II errors is worse?
o
In my opinion, I believe that a Type II, false negative is worse than a Type I error,
as Type II error involves being unable to detect a true effect or what you’re testing
for is essentially hidden in plain sight. Common Type II errors might involve
overlooking significant findings resulting in the rejection of a statistical
hypothesis when the hypothesis would otherwise coincide with the found data and
be accepted.
Do you think the context in which the statistical decision is being made affects which of
the errors is worse? For example, if you think about scientific research into curing cancer,
or jury decisions about criminal convictions, or scheduling decisions to get to work on
time, do you feel that the negative effects of Type I and Type II errors are similar or
different across these contexts?
o
I believe the context surrounding a statistical decision being made and
experiencing a falsehood (Type I and Type II errors) affect scenarios differently,
so it is hard to say which is worse. Regarding the jury’s decision scenario, a type I
error would result in a wrongful conviction, while a type II error could result in a
guilty individual walking free. Regarding the medical test scenario, a type I error
could lead to a patient being treated for an illness they don’t have, a type II error
could result in a patient not being treated for an illness they in fact have. Context
is a big factor when looking at possible consequences of statistical
misunderstanding.
Given your earlier discussion about the importance of statistical thinking for effective
citizenship and what you have learned in the course in general and this module
specifically, do you still hold the same view about the importance of statistical thinking
for the general population? Why or why not?
o
Nearly every module we have worked on through this course has reinforced my
belief that understanding and being able to interpret statistics means that you will
be an effective citizen. I believe statistics are important for the general population
as is allows us to see through biases and misinformation, statistics allow us to see
the bigger picture without having to survey an entire population. This course has
reminded me to be skeptical when facing statistics and how to tell if data has been
sourced to support the researcher’s hypothesis instead of the truth. Understanding
statistics means being able to make decisions, make predictions, and make
discoveries based on data.
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