Business
1. Take-home midterms
a. You. Must. Justify. Your. Answers. That's the whole point of this course. Beliefs and assertions do not equal justifications and arguments.
b. A minority of people (groups?) are still confused about basic concepts: PA, IR, BoP, Sufficiency. Please come see me during office hours so I can help you. Don't wait until it's too late.
c. "More statistics and facts are needed". "Cuz it has facts..." "Cuz it has statistics..." "Cuz it doesn't have enough facts/statistics"
d. Bias vs illegitimate bias.
2. April 4: Last chance to drop. If you're unsure, you should come see me during office hours.
3. READ: Atlantic and Psychiatric Drug Research for Blog Reviews (Optional: the half-life of facts). The links are in the syllabus Week 14.
4. Go Over HW 10B
5. Practice Generalizations and Polling.
6. Practice Fallacies.
Monday, March 31, 2014
Wednesday, March 26, 2014
Lesson 10B: Polling
Polling
Biggest Polling Fails in (US) History
Polling is a subset of generalizations so many of the rules for evaluation and analysis will be the same as in the previous section for generalizations. Polling is a generalization about a specific population's beliefs or attitudes. For example, during election campaigns, the populations in important "battleground" states are usually polled to find out what issues are important to them. Upon hearing the results, the candidate will then remove what's left of his own spine and say whatever that population wants to hear. (Meh! Call me a cynic...)
Suppose I were to conduct a poll of UNLV students to determine their primary motivation for attending university. To begin the evaluation of the poll we'd need to know 3 things:
(a) The sample: Who is in the sample (representativeness) and how big was that sample.
(b) The population: What is the group I'm trying to make the generalization about.
(c) The property in question: What is that belief, attitude, or value I'm trying to attribute to the population.
Recall from the previous section that generalizations can be interpreted as having an (implicit or explicit) argument form. Lets instantiate this argument structure with a hypothetical poll. Suppose I want to poll UNLV students with the question, "should critical thinking 102 be a graduation requirement?" Because I have finite time and energy I can't ask each student at the university. Instead I'll take a sample and extrapolate from that. My sample will be students in my class.
P1. A sample of 36 students from my class is a representative sample of the general student population.
P2. 65% of the students in my class (i.e., the sample) said they agree that critical thinking 102 should be a graduation requirement.
C. Therefore, we can conclude that around 65% of UNLV students think that critical thinking 102 should be a graduation requirement.
The are 2 broad categories of analysis we can apply to the poll results:
Sampling Errors
Questions about sampling errors apply to P1, which are basically: (a) is the sample size large enough to be representative of the group and (b) does the sample avoid any biases (i.e., does it avoid under or over representing one group over another in a way that isn't reflective of the general population).
Regarding sample size, national polls generally require a (representative) sample size of 1000, so we should expect that a poll about the UNLV population could be quite a bit less than that. Aside from that, (a) is self explanatory and I've discussed it above, so lets look a little more closely at (b).
The question here is whether the students in my class accurately represent all important subgroups in the student population. For example, is the sample representative of UNLVs general populations ratio of ethic groups, socio-economic groups, and majors? You might find that there are other important subgroups that should be captured in a sample depending on the content of the poll.
Someone might plausibly argue that the sample isn't representative because it disproportionately represents students in their 1st and 2nd years. A sample that isn't representative of the target population is a called a biased sample.
We can ask a further question about how the group was chosen. For example, if I make filling out the survey voluntary then there's a possibility of bias. Why? Because it's possible that people who volunteer for such a survey have a strong opinion one way or another. This means that the poll will capture only those with strong opinions (or those who just generally like to give their opinion) but leave out the Joe-Schmo population who might not have strong feelings or might be too busy facebooking on their got-tam phone to bother to do the survey. Selection bias is when the way a sample is selected causes sample bias.
In order to protect again such sampling errors polls should engage in random sampling. That means no matter what sub-group someone is in, they have an equal probability of being selected to do the survey. We can also take things to a whole. nuva. level. when we use stratified sampling. With stratified sampling we make sure a representative proportion of each subgroup is contained in the general sample. For example, if I know that about 30% of students are 1st year students then I'll make sure that 30% of my sample randomly samples 1st year students.
Another thing to consider in sampling bias is margin of error. The margin of error (e.g. +/-5%) measures the likelihood that the data collected is dependable. Margin of error is important to consider when there is a small difference between competing results. For example, suppose a survey says 46% of students think Ami should be burned at the stake while 50% say Ami should be hailed as the next messiah. One might think this clearly shows Ami's well on his way to establishing a new religion but we'd be jumping the gun until we looked at the poll's margin of error.
Suppose the margin of error is +/- 5%. This means that those that want to burn Ami at the stake could actually be up to 48.3% ((46x.05)+46) and those that want to make him the head of a new religion could be as low as 47.5% ((50x.05)+50). Ami might have to wait a few more years for world domination.
As I mentioned in the beginning of this section, questions about sampling error are all directed at P1; i.e., is the sample representative of the general population about which the general claim will be made. Next we will look at measurement errors which have to do with the second premise (i..e., that the people in the sample actually do have the believes/attitudes/properties attributed to them in the survey).
Measurement Errors
Measurement errors have to do with scrutinizing the claim that the sample population actually has the believes/attitudes/properties attributed to them in the survey. Evaluating polls for measurement errors generally has to do with how the information was asked/collected, how the questions were worded, and the environmental conditions at the time of the poll.
As a starting point, when we are looking at polls that are about political issues, we should generally be skeptical of results--especially when polling agencies that are tied to a political party or ideologies produce competing poll results that conform with their respective positions. In short, we should be alert to who is conducting the poll and consider whether there may be any biases.
One specific type of measurement error arises out of semantic ambiguity or vagueness. For example, suppose a survey asks if you drive "frequently". This is a subjective term and could be interpreted differently. For some people it might mean 1x a week, for others once a day. A measurement error will be introduced into the data unless this vagueness is cleared up. Because more people probably think of "frequent drinking" as being "more than what I personally drink", the results will be artificially low. They also will not very meaningful because the responses don't mean the same thing.
Another type of measurement error arises when we consider the medium by which the questions are asked. Psychology tells us that people are more likely to tell the truth when asked questions face to face and less so when asked over the phone. Even less so when asked in groups (groupthink).
These considerations will introduce measurement errors; that is, they will cast doubt on whether the members of the sample actually have the quality/view/belief being attributed to them.
When evaluation measurement accuracy we should also consider when and where the poll took place. For example, if, during exam period, students are asked whether they think school is stressful (generally), probably more will answer in the affirmative than if they are asked during the 1st week of the semester.
Also, going back to our poll of students concerning the having critical thinking as a graduation requirement, we might argue that the timing is influencing the results. The sample is taken from students currently taking the class. Perhaps it's too early in their career to appreciate the course's value; yet if we asked students who had already taken the course and have had a chance to enjoy the glorious fruits of the class, the results might be different.
Finally, we should be alert to how second-hand reporting of polls can present the results in a distorted way. Newspapers and media outlets want eyeballs, so they might over-emphasize certain aspects of the poll or interpret the results in a way that sensationalizes them. In short, we should approach with a grain of salt polls that are reported second-hand.
To summarize: ; For polling we want to evaluate (1) is the sample free of (a) sampling errors and (b) sampling measurement errors (i.e., selection bias), and (2) do the individuals in the sample actually have the values/attitudes/beliefs being attributed to them.
Helpful Resource on Polling Errors
Biggest Polling Fails in (US) History
Polling is a subset of generalizations so many of the rules for evaluation and analysis will be the same as in the previous section for generalizations. Polling is a generalization about a specific population's beliefs or attitudes. For example, during election campaigns, the populations in important "battleground" states are usually polled to find out what issues are important to them. Upon hearing the results, the candidate will then remove what's left of his own spine and say whatever that population wants to hear. (Meh! Call me a cynic...)
Suppose I were to conduct a poll of UNLV students to determine their primary motivation for attending university. To begin the evaluation of the poll we'd need to know 3 things:
(a) The sample: Who is in the sample (representativeness) and how big was that sample.
(b) The population: What is the group I'm trying to make the generalization about.
(c) The property in question: What is that belief, attitude, or value I'm trying to attribute to the population.
Recall from the previous section that generalizations can be interpreted as having an (implicit or explicit) argument form. Lets instantiate this argument structure with a hypothetical poll. Suppose I want to poll UNLV students with the question, "should critical thinking 102 be a graduation requirement?" Because I have finite time and energy I can't ask each student at the university. Instead I'll take a sample and extrapolate from that. My sample will be students in my class.
P1. A sample of 36 students from my class is a representative sample of the general student population.
P2. 65% of the students in my class (i.e., the sample) said they agree that critical thinking 102 should be a graduation requirement.
C. Therefore, we can conclude that around 65% of UNLV students think that critical thinking 102 should be a graduation requirement.
The are 2 broad categories of analysis we can apply to the poll results:
Sampling Errors
Questions about sampling errors apply to P1, which are basically: (a) is the sample size large enough to be representative of the group and (b) does the sample avoid any biases (i.e., does it avoid under or over representing one group over another in a way that isn't reflective of the general population).
Regarding sample size, national polls generally require a (representative) sample size of 1000, so we should expect that a poll about the UNLV population could be quite a bit less than that. Aside from that, (a) is self explanatory and I've discussed it above, so lets look a little more closely at (b).
The question here is whether the students in my class accurately represent all important subgroups in the student population. For example, is the sample representative of UNLVs general populations ratio of ethic groups, socio-economic groups, and majors? You might find that there are other important subgroups that should be captured in a sample depending on the content of the poll.
Someone might plausibly argue that the sample isn't representative because it disproportionately represents students in their 1st and 2nd years. A sample that isn't representative of the target population is a called a biased sample.
We can ask a further question about how the group was chosen. For example, if I make filling out the survey voluntary then there's a possibility of bias. Why? Because it's possible that people who volunteer for such a survey have a strong opinion one way or another. This means that the poll will capture only those with strong opinions (or those who just generally like to give their opinion) but leave out the Joe-Schmo population who might not have strong feelings or might be too busy facebooking on their got-tam phone to bother to do the survey. Selection bias is when the way a sample is selected causes sample bias.
In order to protect again such sampling errors polls should engage in random sampling. That means no matter what sub-group someone is in, they have an equal probability of being selected to do the survey. We can also take things to a whole. nuva. level. when we use stratified sampling. With stratified sampling we make sure a representative proportion of each subgroup is contained in the general sample. For example, if I know that about 30% of students are 1st year students then I'll make sure that 30% of my sample randomly samples 1st year students.
Another thing to consider in sampling bias is margin of error. The margin of error (e.g. +/-5%) measures the likelihood that the data collected is dependable. Margin of error is important to consider when there is a small difference between competing results. For example, suppose a survey says 46% of students think Ami should be burned at the stake while 50% say Ami should be hailed as the next messiah. One might think this clearly shows Ami's well on his way to establishing a new religion but we'd be jumping the gun until we looked at the poll's margin of error.
Suppose the margin of error is +/- 5%. This means that those that want to burn Ami at the stake could actually be up to 48.3% ((46x.05)+46) and those that want to make him the head of a new religion could be as low as 47.5% ((50x.05)+50). Ami might have to wait a few more years for world domination.
As I mentioned in the beginning of this section, questions about sampling error are all directed at P1; i.e., is the sample representative of the general population about which the general claim will be made. Next we will look at measurement errors which have to do with the second premise (i..e., that the people in the sample actually do have the believes/attitudes/properties attributed to them in the survey).
Measurement Errors
Measurement errors have to do with scrutinizing the claim that the sample population actually has the believes/attitudes/properties attributed to them in the survey. Evaluating polls for measurement errors generally has to do with how the information was asked/collected, how the questions were worded, and the environmental conditions at the time of the poll.
As a starting point, when we are looking at polls that are about political issues, we should generally be skeptical of results--especially when polling agencies that are tied to a political party or ideologies produce competing poll results that conform with their respective positions. In short, we should be alert to who is conducting the poll and consider whether there may be any biases.
One specific type of measurement error arises out of semantic ambiguity or vagueness. For example, suppose a survey asks if you drive "frequently". This is a subjective term and could be interpreted differently. For some people it might mean 1x a week, for others once a day. A measurement error will be introduced into the data unless this vagueness is cleared up. Because more people probably think of "frequent drinking" as being "more than what I personally drink", the results will be artificially low. They also will not very meaningful because the responses don't mean the same thing.
Another type of measurement error arises when we consider the medium by which the questions are asked. Psychology tells us that people are more likely to tell the truth when asked questions face to face and less so when asked over the phone. Even less so when asked in groups (groupthink).
These considerations will introduce measurement errors; that is, they will cast doubt on whether the members of the sample actually have the quality/view/belief being attributed to them.
When evaluation measurement accuracy we should also consider when and where the poll took place. For example, if, during exam period, students are asked whether they think school is stressful (generally), probably more will answer in the affirmative than if they are asked during the 1st week of the semester.
Also, going back to our poll of students concerning the having critical thinking as a graduation requirement, we might argue that the timing is influencing the results. The sample is taken from students currently taking the class. Perhaps it's too early in their career to appreciate the course's value; yet if we asked students who had already taken the course and have had a chance to enjoy the glorious fruits of the class, the results might be different.
Finally, we should be alert to how second-hand reporting of polls can present the results in a distorted way. Newspapers and media outlets want eyeballs, so they might over-emphasize certain aspects of the poll or interpret the results in a way that sensationalizes them. In short, we should approach with a grain of salt polls that are reported second-hand.
To summarize: ; For polling we want to evaluate (1) is the sample free of (a) sampling errors and (b) sampling measurement errors (i.e., selection bias), and (2) do the individuals in the sample actually have the values/attitudes/beliefs being attributed to them.
Helpful Resource on Polling Errors
Tuesday, March 25, 2014
Lecture 10B: Polling
Generalizations Con't
Review general structure, representativeness, sample size, sample bias, selection bias, measurement problems.
Criticizing Premise 2: Measurement problems: Are we measuring the property that we think we're measuring?
E.g. Religion and Happiness
E.g. Trolly Dilemma (a) interpretations of deontologists (b) interpretations of utilitarians
2 interpretations
E.g. The voting machines are rigged!!!1!!1!!!
Polling
Definition: Polling is a generalization about a specific population's beliefs or attitudes.
Poll: Guns and Aliens
Polling requires that we know 3 things:
(b) The (target) population: What is the group I'm trying to make the generalization about.
(c) The property in question: What is that belief, attitude, or value I'm trying to attribute to the population.
The underlying formal structure of a polling argument is the same as that of a generalization.
(P1) S is a representative sample of Xs.
(P2) Proportion 1 of Xs in S have property Y (have attitude/belief Y).
(C) Proportion 2 of Xs have the property Y (have attitude/belief Y).
Measurement Problems
1. How you ask the question and how the audience interprets the question can affect the results of a poll.
Younger Democrats overwhelmingly supported ObamaCare, with 68 percent approval, while only 26 percent of independents and 5 percent of Republicans supported it. Language also affected the numbers, particularly among Democrats and independents. When asked if they supported the "Affordable Care Act," as opposed to "ObamaCare," 81 percent of Democrats approved, while 34 percent of independents and 7 percent of Republicans said they were in favor.
Harvard Poll
2. There can be a difference in how the results of a poll are reported and the quality or property that is actually being measured by the poll. Also, how are the target groups defined?
http://online.wsj.com/article/PR-CO-20131023-913504.html
(a) P1 L4
(b) About this Research L2.
3. Alt-med usage poll
The conclusion, the list, Sec: Interview Para 4, 5, 6.
4. Poll on evolution
Clinton Voters
Trump Voters
Another problem with poll data (People will give answers even though they have no clue what they're talking about)
6. Loaded Questions and Setting the Tone
E.g., Would you agree that Obama is doing a poor job?
E.g., Wouldn't you agree that Critical Thinking 102 is an important course? (Newspaper Headline: Students Agree: Ami's Critical Thinking Course Is the Most Important Course in the World)
E.g., Over the last 2 years the economy has crashed and our national security is more vulnerable than ever. How would you rate G. W. Bush's performance as president?
7. Measurement problems because of language problems (e.g., vagueness, ambiguity)
E.g., Do you drink alcohol frequently, moderately, hardly at all, never?
8. Mathematical problems/measurement problems: Teacher Evaluation
Tip for Measurement Problems: Track down the original poll being quoted and read the "Interview" Section to find out what the actual questions were.
People can be freakin' liars: (Jimmy Kimmel super Tuesday California)
<div id="fb-root"></div><script>(function(d, s, id) { var js, fjs = d.getElementsByTagName(s)[0]; if (d.getElementById(id)) return; js = d.createElement(s); js.id = id; js.src = "//connect.facebook.net/en_US/sdk.js#xfbml=1&version=v2.3"; fjs.parentNode.insertBefore(js, fjs);}(document, 'script', 'facebook-jssdk'));</script><div class="fb-video" data-allowfullscreen="1" data-href="/JimmyKimmelLive/videos/vb.195974498373/10153939666333374/?type=3"><div class="fb-xfbml-parse-ignore"><blockquote cite="https://www.facebook.com/JimmyKimmelLive/videos/10153939666333374/"><a href="https://www.facebook.com/JimmyKimmelLive/videos/10153939666333374/">Lie Witness News - Super Tuesday Edition</a><p>We asked people in Hollywood if they voted for #SuperTuesday - they lied. #LieWitnessNews</p>Posted by <a href="https://www.facebook.com/JimmyKimmelLive/">Jimmy Kimmel Live</a> on Wednesday, March 2, 2016</blockquote></div></div>
Effect of Timing: (Availability Bias, Affect Bias)
Poll on civil liberties and security 2011
Harris Poll found that 68% of Americans supported a national ID system. A study conducted in November 2001 for the Washington Post found that only 44% of Americans supported national ID. A poll released in March 2002 by the Gartner Group found that 26% of Americans favored a national ID, and that 41% opposed the idea. Popular support for other surveillance technologies has declined as well.
2. There can be a difference in how the results of a poll are reported and the quality or property that is actually being measured by the poll. Also, how are the target groups defined?
http://online.wsj.com/article/PR-CO-20131023-913504.html
(a) P1 L4
(b) About this Research L2.
3. Alt-med usage poll
The conclusion, the list, Sec: Interview Para 4, 5, 6.
4. Poll on evolution
5.
Clinton Voters
Trump Voters
Another problem with poll data (People will give answers even though they have no clue what they're talking about)
6. Loaded Questions and Setting the Tone
E.g., Would you agree that Obama is doing a poor job?
E.g., Wouldn't you agree that Critical Thinking 102 is an important course? (Newspaper Headline: Students Agree: Ami's Critical Thinking Course Is the Most Important Course in the World)
E.g., Over the last 2 years the economy has crashed and our national security is more vulnerable than ever. How would you rate G. W. Bush's performance as president?
7. Measurement problems because of language problems (e.g., vagueness, ambiguity)
E.g., Do you drink alcohol frequently, moderately, hardly at all, never?
8. Mathematical problems/measurement problems: Teacher Evaluation
A. What grade do you expect in this class?
B. What grade do you deserve in this class?
B. What grade do you deserve in this class?
50% of the variance in student overall evaluations could be explained by the difference between those two quantities. If they were nearly equal, the evaluations tended to be good. If A was much less than B, they tended to be bad. And, of course, as we increasingly succumb to grade inflation, our teacher evaluations are improving.
Tip for Measurement Problems: Track down the original poll being quoted and read the "Interview" Section to find out what the actual questions were.
People can be freakin' liars: (Jimmy Kimmel super Tuesday California)
<div id="fb-root"></div><script>(function(d, s, id) { var js, fjs = d.getElementsByTagName(s)[0]; if (d.getElementById(id)) return; js = d.createElement(s); js.id = id; js.src = "//connect.facebook.net/en_US/sdk.js#xfbml=1&version=v2.3"; fjs.parentNode.insertBefore(js, fjs);}(document, 'script', 'facebook-jssdk'));</script><div class="fb-video" data-allowfullscreen="1" data-href="/JimmyKimmelLive/videos/vb.195974498373/10153939666333374/?type=3"><div class="fb-xfbml-parse-ignore"><blockquote cite="https://www.facebook.com/JimmyKimmelLive/videos/10153939666333374/"><a href="https://www.facebook.com/JimmyKimmelLive/videos/10153939666333374/">Lie Witness News - Super Tuesday Edition</a><p>We asked people in Hollywood if they voted for #SuperTuesday - they lied. #LieWitnessNews</p>Posted by <a href="https://www.facebook.com/JimmyKimmelLive/">Jimmy Kimmel Live</a> on Wednesday, March 2, 2016</blockquote></div></div>
Effect of Timing: (Availability Bias, Affect Bias)
Poll on civil liberties and security 2011
Harris Poll found that 68% of Americans supported a national ID system. A study conducted in November 2001 for the Washington Post found that only 44% of Americans supported national ID. A poll released in March 2002 by the Gartner Group found that 26% of Americans favored a national ID, and that 41% opposed the idea. Popular support for other surveillance technologies has declined as well.
Consider polls on gun control post-major public shooting, major environmental catastrophe, major natural disaster, asking students about the stressfulness of school during exam periods, etc...
Tip: Look at the date the poll was conducted vs the date it was published.
Margin of Error
Poll says the We Love America More than You Party has 49% of the vote while the American Freedom Party has 44% of the vote. There is a margin of error of +/- 3%. Supposing the data perfectly reflects how people will actually vote, is the American Freedom Party guaranteed to win?
Tip: When results are close, find the margin of error.
Sample Bias (Criticizing Premise 1)
Sample bias is when the characteristics of members of sample aren't (proportionally) representative of the relevant characteristics of the group.
https://twitter.com/realDonaldTrump/status/788954581346779136/photo/1?ref_src=twsrc%5Etfw
https://twitter.com/realDonaldTrump/status/788954581346779136/photo/1?ref_src=twsrc%5Etfw
Stratified Random Sampling helps to avoid sample bias. Make sure all relevant subgroups are represented through random sampling + weighting of subgroups.
Question: How would you stratify a sample for a survey of American opinions?
Selection Bias (Criticizing Premise 1)
Sample bias + sample size.
When the way a sample is chosen causes sample bias (often inadvertently) this is called selection bias. If possible, track down the original poll being quoted read the methods and interview section.
When the way a sample is chosen causes sample bias (often inadvertently) this is called selection bias. If possible, track down the original poll being quoted read the methods and interview section.
E.g., Alt-med study with non-English speakers
E.g., Doctors on aiding executions (response rates, state policy)
E.g., Internet surveys, landline surveys
E.g., Literary Digest predicts 370 to 161 for Landon vs Roosevelt.
E.g., Right-wing polls re: first debate vs Prediction markets
E.g., Right-wing polls re: first debate vs Prediction markets
Common causes of selection bias: ideological polling group, self-selection, pre-screening of trial participants, discounting trial subjects/tests that did not run to completion and migration bias by excluding subjects who have recently moved into or out of the study area.
How to avoid sample bias: Make sure all relevant subgroups are represented through random sampling + weighting.
Tip: If possible, track down the original poll being quoted read the methods and interview section.
How Were the Results Reported in the Media/Headline vs What Does the Actual Poll/Article Say?
Monday, March 24, 2014
Lecture 10A: Generalizations
Business
1. Podcast/Blog Reviews.
2. Discuss ACA arguments.
3. Generalizations
Generalizations
Definition: A generalization is the process of moving from some specific observations about individuals (or instances of a type of event) within a group to a general claim about members of the whole group (or instances of that type of event).
Statistical Syllogism vs Enumerative Induction
General Claims vs Universal Claims:
Universal claims are guarantees: E.g. All ravens are black, I always eat peanut butter and toast for breakfast, Everytime I see you falling I get down on my knees and pray...
General claims are probabilistic: E.g., 76% of SNAP households included a child, an elderly person, or a disabled person. These vulnerable households receive 83% of all SNAP benefits
http://www.fns.usda.gov/ora/menu/Published/snap/SNAPPartHH.htm
Most UNLV students like pizza, only a small portion of the Vegas population has been to Panama, only about 1/3 of all students who begin 2 year degrees complete them within 3 years.
Mini Phil of Science Problem: How big of a sample do you need? (Drug test vs Stove is hot).
Examples of Generalizations
spurious causation
A
Informal Presentation: 10% of Ami's students are wearing red today therefore around 10% of UNLV students must be wearing red today.
Formal Structure
(P1) The students in Ami's class are representative of all UNLV students.
(P2) 10% of the students in Ami's class are wearing red today.
(C) Therefore around 10% of UNLV students must be wearing red today.
B
75% of my friends at school have student loans therefore 75% of students at UNLV have student loans.
C
All my friends have happier and more interesting lives than mine. Every time I check my facebook feed, they're posting about doing something interesting or fun. My life sucks.
D
Conrad Hilton started out dirt poor and became super-rich, therefore anyone can do it.
E
We asked anyone who was motivated to lose weight to try our new magic diet of eating only natural organic birch tree bark. Over 80% of participants lost weight. 80% of people who try our new magic diet will lose weight.
E
Fox just did a call-in telephone poll of over 10 000 people and 80% of them agreed that Obama is doing a terrible job. That shows that around 80% of Americans think Obama's doing a horrible job.
F
If you're sick you should use this homeopathic remedy. It worked for me last time I was sick.
H
1/3 of students in two-year programs at Washington State community colleges graduate within 3 years. Therefore, about 30% of people in 2 year programs graduate within 3 years
G
Trolly Dilemma (from 2:00) and fMRI: People who make the utilitarian choice are better moral reasoners. +What is the fMRI measuring?
H
In the ultimatum game, most studies in the best American universities have shown that most subjects offer between 30 to 50% and will usually reject anything below around 30%. Therefore, the human notion of fairness is that when playing the ultimatum game, you should give 30-50% to be fair.
I
http://tylervigen.com/view_correlation?id=677
spurious correlations
J
K. Putting it all together:
Diet soda and gut bacteria
Americans Are WIERD (Western, Educated, Industrialized, Rich, and Democratic)
Ultimatum Game: Americans vs Machiguenga vs Gift Giving Cultures vs standard game theoretic model (Homo economicus)
"[...] their goal was not to say that one culturally shaped psychology was better or worse than another—only that we’ll never truly understand human behavior and cognition until we expand the sample pool beyond its current small slice of humanity. "
1. Podcast/Blog Reviews.
2. Discuss ACA arguments.
3. Generalizations
Generalizations
Definition: A generalization is the process of moving from some specific observations about individuals (or instances of a type of event) within a group to a general claim about members of the whole group (or instances of that type of event).
Statistical Syllogism vs Enumerative Induction
General Claims vs Universal Claims:
Universal claims are guarantees: E.g. All ravens are black, I always eat peanut butter and toast for breakfast, Everytime I see you falling I get down on my knees and pray...
General claims are probabilistic: E.g., 76% of SNAP households included a child, an elderly person, or a disabled person. These vulnerable households receive 83% of all SNAP benefits
http://www.fns.usda.gov/ora/menu/Published/snap/SNAPPartHH.htm
Most UNLV students like pizza, only a small portion of the Vegas population has been to Panama, only about 1/3 of all students who begin 2 year degrees complete them within 3 years.
Mini Phil of Science Problem: How big of a sample do you need? (Drug test vs Stove is hot).
Examples of Generalizations
spurious causation
A
Informal Presentation: 10% of Ami's students are wearing red today therefore around 10% of UNLV students must be wearing red today.
Formal Structure
(P1) The students in Ami's class are representative of all UNLV students.
(P2) 10% of the students in Ami's class are wearing red today.
(C) Therefore around 10% of UNLV students must be wearing red today.
B
75% of my friends at school have student loans therefore 75% of students at UNLV have student loans.
C
All my friends have happier and more interesting lives than mine. Every time I check my facebook feed, they're posting about doing something interesting or fun. My life sucks.
D
Conrad Hilton started out dirt poor and became super-rich, therefore anyone can do it.
E
We asked anyone who was motivated to lose weight to try our new magic diet of eating only natural organic birch tree bark. Over 80% of participants lost weight. 80% of people who try our new magic diet will lose weight.
E
Fox just did a call-in telephone poll of over 10 000 people and 80% of them agreed that Obama is doing a terrible job. That shows that around 80% of Americans think Obama's doing a horrible job.
F
If you're sick you should use this homeopathic remedy. It worked for me last time I was sick.
H
1/3 of students in two-year programs at Washington State community colleges graduate within 3 years. Therefore, about 30% of people in 2 year programs graduate within 3 years
G
Trolly Dilemma (from 2:00) and fMRI: People who make the utilitarian choice are better moral reasoners. +What is the fMRI measuring?
H
In the ultimatum game, most studies in the best American universities have shown that most subjects offer between 30 to 50% and will usually reject anything below around 30%. Therefore, the human notion of fairness is that when playing the ultimatum game, you should give 30-50% to be fair.
I
http://tylervigen.com/view_correlation?id=677
spurious correlations
J
K. Putting it all together:
Diet soda and gut bacteria
Americans Are WIERD (Western, Educated, Industrialized, Rich, and Democratic)
Ultimatum Game: Americans vs Machiguenga vs Gift Giving Cultures vs standard game theoretic model (Homo economicus)
"[...] their goal was not to say that one culturally shaped psychology was better or worse than another—only that we’ll never truly understand human behavior and cognition until we expand the sample pool beyond its current small slice of humanity. "
"At its heart, the challenge of the WEIRD paper is not simply to the field of experimental human research (do more cross-cultural studies!); it is a challenge to our Western conception of human nature."
Interesting Generalizations
Veterans and Homelessness
Homelessness Stats
Tipping
What property are we measuring?
race and private prisons
Key Concepts
Structure of Generalizations
(P1) S is a sample of Xs.
(P2) Proportion 1 of Xs in S are Y.
(C) Proportion 2 of Xs are Y.
Structure of Statistical Syllogism
(P1) Proportion 1 of Xs are Y.
(P2) S is a sample of Xs.
(C) Proportion 2 of Xs in S are Y.
Target Group/Population: The whole group of things the generalization is about.
Sample: The observed of selected members of the whole target group.
Relevant Property: The property of the group we are studying.
Representativeness: The degree to which the sample resembles--in relevant ways--the target group.
How to Analyze/Criticize a Generalization: Like all inductive arguments, generalizations can range from anywhere between weak and strong. Its strength will depend on how well it passes the following tests:
(a) Is the sample size large enough? If not, then it is a hasty generalization. Arguments using anecdotal evidence commit this fallacy.
(b) Is the sample representative of the group? I.e., Does the sample have all the same relevant characteristics of the target group? If not then there is sample bias. Volunteer subjects in a study may not be representative of the population being studied, as a consequence, the results of the study may not be generizable to the entire population.
(c) Does the way the sample was selected cause bias (I.e., non-randomly)? If yes, then there is selection bias: SELECTION BIAS: occurs when the subject CHOOSES whether to enter a drug group or a placebo group rather than being randomly assigned. OR, the investigator purposely CHOOSES to put a subject in a drug or placebo group.
(d) Is the property being attributed to the sample actually the property being measured? (I.e., Do Xs really have property Y?) If not then there's a measurement problem.
For statistical syllogisms:
(a) Is S representative of X?
(b) Is the variation in concentrations of properties across Xs. (Distribution)
(c) Are there important subdivisions of X (the target group).
Homework 10A
P. 232 Ex 9A Do Q1 Pick any 4; Q2 all
Learn from the pros: Read: Effects of Diet Soda on Gut Bacteria
Interesting Generalizations
Veterans and Homelessness
Homelessness Stats
Tipping
What property are we measuring?
race and private prisons
Key Concepts
Structure of Generalizations
(P1) S is a sample of Xs.
(P2) Proportion 1 of Xs in S are Y.
(C) Proportion 2 of Xs are Y.
Structure of Statistical Syllogism
(P1) Proportion 1 of Xs are Y.
(P2) S is a sample of Xs.
(C) Proportion 2 of Xs in S are Y.
Target Group/Population: The whole group of things the generalization is about.
Sample: The observed of selected members of the whole target group.
Relevant Property: The property of the group we are studying.
Representativeness: The degree to which the sample resembles--in relevant ways--the target group.
How to Analyze/Criticize a Generalization: Like all inductive arguments, generalizations can range from anywhere between weak and strong. Its strength will depend on how well it passes the following tests:
(a) Is the sample size large enough? If not, then it is a hasty generalization. Arguments using anecdotal evidence commit this fallacy.
(b) Is the sample representative of the group? I.e., Does the sample have all the same relevant characteristics of the target group? If not then there is sample bias. Volunteer subjects in a study may not be representative of the population being studied, as a consequence, the results of the study may not be generizable to the entire population.
(c) Does the way the sample was selected cause bias (I.e., non-randomly)? If yes, then there is selection bias: SELECTION BIAS: occurs when the subject CHOOSES whether to enter a drug group or a placebo group rather than being randomly assigned. OR, the investigator purposely CHOOSES to put a subject in a drug or placebo group.
(d) Is the property being attributed to the sample actually the property being measured? (I.e., Do Xs really have property Y?) If not then there's a measurement problem.
For statistical syllogisms:
(a) Is S representative of X?
(b) Is the variation in concentrations of properties across Xs. (Distribution)
(c) Are there important subdivisions of X (the target group).
Homework 10A
P. 232 Ex 9A Do Q1 Pick any 4; Q2 all
Learn from the pros: Read: Effects of Diet Soda on Gut Bacteria
Lesson 10A: Generalizations
Introduction
In the course of most arguments, factual or empirical claims will be made. A factual or empirical claim is one that can actually or in theory be observed and tested. Two common and closely related types of empirical claims are generalization and is polling. Lets look at each in turn.
Generalizations
A generalization is when an arguer moves from observations about some specific phenomena or objects to a general claim about all phenomena or objects belonging to that group. For example, I go to McDonald's and order a Big Mac and it costs $1.50. Then I go to another McDonald's and order another Big Mac and it also costs $1.50. Based on these observations I generalize to the conclusion that all McDonald's restaurants will change $1.50 for a Big Mac. (I'm such a great scientist)
Another example would be if I ordered 1000 Tshirts that say "I Love Soviet Uzbekistan". Upon receiving the order I might look at 5-10 of the shirts in 2 or 3 of the 10 boxes to make sure they were printed properly. From those specific samples I'd generalize to the conclusion that all the Tshirts were printed correctly. If I'd found printing errors, I'd check more boxes to get a better idea of the proportion of total shirts with printing errors.
There's nothing really fancy going on with generalizations. We all do it a lot in our everyday lives because it's practical and often it wouldn't make sense to do otherwise.
General vs Universal Claims
At this point we should make a distinction between a general claim and a universal claim. A universal claim is that all X's have the property Y. For example, all humans have a heart and a brain. Universal claims are much stronger than general claims.
General claims admit of exceptions but are generally true of a set of objects. For example, generally students like to sleep. We could possibly point out some counter-examples, like Jittery Joe who doesn't like to sleep. But, despite the occasional exception, we can accept the generalization as true.
Lets get technical for a moment and formalize these structures:
A universal claim will generally ;) have the form, "all Xs are Y".
A general claim will generally have the form, "Xs are, in general Y," or "Xs are Y," or "Each X is probably Y".
Sometimes, (surprisingly) in conversation or in an article, the arguer won't spell out for you or use the exact language I've specified here to distinguish between the two types of claims, nevertheless, if you pay attention to context, you should be able to determine which is being made.
The main point to understand is that universal claims don't allow any exceptions whereas general claims do. Also, from the point of view of constructing and evaluating arguments, it is much more difficult to defend an universal claim than a general claim.
One last type of generalization is the proportional claim: As you might expect, this type of claim expreses a proportion. For example, looking through the first 2 boxes of my Tshirts, I notice that 1 out of every 7 is missing the letter "I". So, even though I don't check the remaining boxes I conclude that 1 out 7 of the Tshirts in those boxes is also missing an "I". (i.e., I made a proportional generalization).
The thing is, (as you might expect) there are legitimate and illegitimate generalizations which have much to do with the nature and size of the sample from which the generalization is being made.
Sample Size Me!
For obvious reasons, the larger the sample size, the more accurately it will reflect the properties in the entire group of objects. For instance, if I see one student and she tells me she has student loans, I shouldn't conclude that all students have loans. Maybe I talk to 3 students and they also tell me they have student loans. It could be that I just happened to talk to the 3 students that have student loans, it doesn't mean that all students have them. The sample is still too small for me to legitimately make any inferences about all the students.
Now suppose I talk to 400 students and 100 of them (amazing round numbers!) tell me they have student loans. At this point I might be able to make a reasonable generalization about all students at that particular school or maybe in that particular region or state.
Sample Bias and Representativeness
One worry is that our sample is too small to justify generalizations. The other is that our sample isn't representative enough of the group about which we are making the generalization. For instance, if I wanted to make a generalization about the proportion of US students with student loans, it wouldn't be enough to collect data at only one school.
My sample have to have about the same proportion of sub-groups as does the general population I want to generalize about. Maybe my sample happens to be from a rich school. Maybe not. Either way, this doesn't represent the average school. Maybe that particular state provides excellent funding, maybe not. Again, I want to make sure my sample represents the the proportion of states or schools that do and don't provide excellent funding. I also want to make sure my sample includes the demographics of all US students in about the same proportion.
To have a representative sample of the larger group, what is needed is to take samples from all over the country. That is, the sample from which we will generalize should be broad enough to negate 'clumpy-ness' of certain traits and should be representative of the group we are trying to generalize about.
In terms of evaluating and constructing arguments beware of anecdotal evidence! Why? Remember biases? Biases have a huge influence over what gets reported and what doesn't. If we experience something that runs against our bias we tend to ignore it. While on the other hand, we over emphasize experiences that conform to our biases. When we are using testimony as evidence (i.e. anecdotal evidence), we should be aware of this and how it increases the likelihood that our sample is biased (and therefore not representative of the group of things we are generalizing about).
Here are some common sources/common examples of bias: "it worked for me (or my Aunt Martha), therefore it works for everyone".
The problem with this is that your sample size is exactly 1. If you are going to use a sample size of 1 for a generalization about medicine/treatment or anything that should apply to everyone, then your sample is worth exactly nothing!
Another common bias (and a huge issue/problem in social psychology right now) is generalizing about all human behavior from samples that have a geographical and cultural bias.
In other words, for decades social psychologists and psychologists have been making generalizations about all of human psychology from samples of US college students. As it turns out (from recent cross-cultural studies) US culture is an outlier in terms of what's "normal" psychology throughout the world. Yup. We're the weird ones, not the rest of the world. Oh! Snap! (but of course our way of thinking is the right way!)
In medicine, great lengths are gone to to protect against a biased sample. The gold standard is a large (3 to 5 thousand subjects) double-blind, placebo controlled, long-term replicated study that includes different populations (i.e., ethnic groups) and both sexes. The hallmark of pseudoscience in medicine is that often these standards are not applied or the sample is too small.
Rules for Good Generalizations
We can think of generalizations as following (implicitly or explicitly) this argument scheme:
(S=the sample group, X=the entire group of objects that the generalization will be about, Y=the property we're attributing to Xs)
P1. S is a sample of Xs.
P2. The proportion of Ss (that are part of X) that have property Y is Z.
C. The proportion of Xs that have property Y is Z*.
*see rule 4 below.
Lets use an actual example to get away from the alphabet soup:
P1. The students in this class are a (representative) sample of UNLV students.
P2. The proportion of the students in the class that have student loans is 60%.
C. Therefore, the proportion of UNLV students with loans is around 60%.
Or
P1 10 species of cats is a sample of all the species of all cats.
P2 The proportion of cats in the sample that land on their feet when dropped from over 4 feet is 100%.
C Therefore, all species of cats will land on their feet when dropped from over 4 feet.
To evaluate generalizations we essentially want to scrutinize P1 and P2 and their logical connection to C. To do so we ask if
1) The sample size is reasonable for the scope of the generalization.
2) The sample avoids biases.
3) Objects/Phenomena in the sample (X) do indeed have the property Y.
4) The proportion of X with property Y in the sample is greater or equal to the claim about the proportion of Xs with property Y in the generalization. (In other words, I can't say that 30% of Xs in my sample have property Y, yet in generalize that therefore 40% of Xs have property Y.)
If a generalization violates one of these 4 criteria then it likely isn't a defensible generalization.
In the course of most arguments, factual or empirical claims will be made. A factual or empirical claim is one that can actually or in theory be observed and tested. Two common and closely related types of empirical claims are generalization and is polling. Lets look at each in turn.
Generalizations
A generalization is when an arguer moves from observations about some specific phenomena or objects to a general claim about all phenomena or objects belonging to that group. For example, I go to McDonald's and order a Big Mac and it costs $1.50. Then I go to another McDonald's and order another Big Mac and it also costs $1.50. Based on these observations I generalize to the conclusion that all McDonald's restaurants will change $1.50 for a Big Mac. (I'm such a great scientist)
Another example would be if I ordered 1000 Tshirts that say "I Love Soviet Uzbekistan". Upon receiving the order I might look at 5-10 of the shirts in 2 or 3 of the 10 boxes to make sure they were printed properly. From those specific samples I'd generalize to the conclusion that all the Tshirts were printed correctly. If I'd found printing errors, I'd check more boxes to get a better idea of the proportion of total shirts with printing errors.
There's nothing really fancy going on with generalizations. We all do it a lot in our everyday lives because it's practical and often it wouldn't make sense to do otherwise.
General vs Universal Claims
At this point we should make a distinction between a general claim and a universal claim. A universal claim is that all X's have the property Y. For example, all humans have a heart and a brain. Universal claims are much stronger than general claims.
General claims admit of exceptions but are generally true of a set of objects. For example, generally students like to sleep. We could possibly point out some counter-examples, like Jittery Joe who doesn't like to sleep. But, despite the occasional exception, we can accept the generalization as true.
Lets get technical for a moment and formalize these structures:
A universal claim will generally ;) have the form, "all Xs are Y".
A general claim will generally have the form, "Xs are, in general Y," or "Xs are Y," or "Each X is probably Y".
Sometimes, (surprisingly) in conversation or in an article, the arguer won't spell out for you or use the exact language I've specified here to distinguish between the two types of claims, nevertheless, if you pay attention to context, you should be able to determine which is being made.
The main point to understand is that universal claims don't allow any exceptions whereas general claims do. Also, from the point of view of constructing and evaluating arguments, it is much more difficult to defend an universal claim than a general claim.
One last type of generalization is the proportional claim: As you might expect, this type of claim expreses a proportion. For example, looking through the first 2 boxes of my Tshirts, I notice that 1 out of every 7 is missing the letter "I". So, even though I don't check the remaining boxes I conclude that 1 out 7 of the Tshirts in those boxes is also missing an "I". (i.e., I made a proportional generalization).
The thing is, (as you might expect) there are legitimate and illegitimate generalizations which have much to do with the nature and size of the sample from which the generalization is being made.
Sample Size Me!
For obvious reasons, the larger the sample size, the more accurately it will reflect the properties in the entire group of objects. For instance, if I see one student and she tells me she has student loans, I shouldn't conclude that all students have loans. Maybe I talk to 3 students and they also tell me they have student loans. It could be that I just happened to talk to the 3 students that have student loans, it doesn't mean that all students have them. The sample is still too small for me to legitimately make any inferences about all the students.
Now suppose I talk to 400 students and 100 of them (amazing round numbers!) tell me they have student loans. At this point I might be able to make a reasonable generalization about all students at that particular school or maybe in that particular region or state.
Sample Bias and Representativeness
One worry is that our sample is too small to justify generalizations. The other is that our sample isn't representative enough of the group about which we are making the generalization. For instance, if I wanted to make a generalization about the proportion of US students with student loans, it wouldn't be enough to collect data at only one school.
My sample have to have about the same proportion of sub-groups as does the general population I want to generalize about. Maybe my sample happens to be from a rich school. Maybe not. Either way, this doesn't represent the average school. Maybe that particular state provides excellent funding, maybe not. Again, I want to make sure my sample represents the the proportion of states or schools that do and don't provide excellent funding. I also want to make sure my sample includes the demographics of all US students in about the same proportion.
To have a representative sample of the larger group, what is needed is to take samples from all over the country. That is, the sample from which we will generalize should be broad enough to negate 'clumpy-ness' of certain traits and should be representative of the group we are trying to generalize about.
In terms of evaluating and constructing arguments beware of anecdotal evidence! Why? Remember biases? Biases have a huge influence over what gets reported and what doesn't. If we experience something that runs against our bias we tend to ignore it. While on the other hand, we over emphasize experiences that conform to our biases. When we are using testimony as evidence (i.e. anecdotal evidence), we should be aware of this and how it increases the likelihood that our sample is biased (and therefore not representative of the group of things we are generalizing about).
Here are some common sources/common examples of bias: "it worked for me (or my Aunt Martha), therefore it works for everyone".
The problem with this is that your sample size is exactly 1. If you are going to use a sample size of 1 for a generalization about medicine/treatment or anything that should apply to everyone, then your sample is worth exactly nothing!
Another common bias (and a huge issue/problem in social psychology right now) is generalizing about all human behavior from samples that have a geographical and cultural bias.
In other words, for decades social psychologists and psychologists have been making generalizations about all of human psychology from samples of US college students. As it turns out (from recent cross-cultural studies) US culture is an outlier in terms of what's "normal" psychology throughout the world. Yup. We're the weird ones, not the rest of the world. Oh! Snap! (but of course our way of thinking is the right way!)
In medicine, great lengths are gone to to protect against a biased sample. The gold standard is a large (3 to 5 thousand subjects) double-blind, placebo controlled, long-term replicated study that includes different populations (i.e., ethnic groups) and both sexes. The hallmark of pseudoscience in medicine is that often these standards are not applied or the sample is too small.
Rules for Good Generalizations
We can think of generalizations as following (implicitly or explicitly) this argument scheme:
(S=the sample group, X=the entire group of objects that the generalization will be about, Y=the property we're attributing to Xs)
P1. S is a sample of Xs.
P2. The proportion of Ss (that are part of X) that have property Y is Z.
C. The proportion of Xs that have property Y is Z*.
*see rule 4 below.
Lets use an actual example to get away from the alphabet soup:
P1. The students in this class are a (representative) sample of UNLV students.
P2. The proportion of the students in the class that have student loans is 60%.
C. Therefore, the proportion of UNLV students with loans is around 60%.
Or
P1 10 species of cats is a sample of all the species of all cats.
P2 The proportion of cats in the sample that land on their feet when dropped from over 4 feet is 100%.
C Therefore, all species of cats will land on their feet when dropped from over 4 feet.
To evaluate generalizations we essentially want to scrutinize P1 and P2 and their logical connection to C. To do so we ask if
1) The sample size is reasonable for the scope of the generalization.
2) The sample avoids biases.
3) Objects/Phenomena in the sample (X) do indeed have the property Y.
4) The proportion of X with property Y in the sample is greater or equal to the claim about the proportion of Xs with property Y in the generalization. (In other words, I can't say that 30% of Xs in my sample have property Y, yet in generalize that therefore 40% of Xs have property Y.)
If a generalization violates one of these 4 criteria then it likely isn't a defensible generalization.
Tuesday, March 11, 2014
Lecture 8B: Rhetorical Devices, Definitions, Emotional vs Cognitive Meaning, Weasel Words
Business:
1. Get through the lesson quickly. Most of the concepts you will already be familiar with.
2. Drink coffee and eat donuts.
2. Work on the midterms.
Part 1
Examples:
A: You may have already won a brand new car!
B: Some doctors recommend ginko biloba for improved cognitive function.
C: This is perhaps the best diet product ever made!
D: Up to 2/3rds of all people who used Chemain de Fer face cream said they thought they looked younger after just one week!
E: Our best deal yet! This Saturday get up to 50% off!
F: This ancient Chinese medicinal tea relieves joint soreness in up to 60% of people who tried it!
G: As many as 10 students will receive A's in Ami's class!
H: Buy Ami's Non-GMO All-Natural Organic Dirt! Just one application and your fat/acne will seem to melt away/disappear over night!
I: This drink from acai berries from the ancient Amazon rainforest will virtually change your health for the better in a matter of days!
J. BGSU named one of America's best colleges!!!!!http://www.bgsu.edu/news/2016/09/bgsu-named-one-of-americas-best-colleges.html
Weasel words are words that are used to appear to make a strong claim but avoid outright lying. Common weasel words are: "up to x percent/x number", "some", "as many as", "reportedly", "virtually", "many", "seems", "perhaps".
Part 2:
Examples:
A: Happiness Double Joy paper towels are 25% more absorbent!
B:
C: It's Christmas in July! All Dell computers sold below suggested retail price!
D: Phone company X lets you call anywhere cheaper. Just 5 cents per minute compared to Phone Company Y, which charges 10 cents a minute (Lewis Vaughn).
Misleading Comparisons: Often comparisons can mislead by omitting what something is being compared to (E.g., A&C), comparing apples to oranges (E.g., D), or puffery (E.g., B) (legal term in advertising law for hype that few people would take seriously).
Part 3a
A: I'm fairly certain my students with laptops aren't on facebook at this particular moment.
B: I think it's pretty safe to assume Mr. X is a responsible teacher. He hasn't done any drugs in quite a while.
C: Are you talking about politician X? I think that it's great that he's gone as far as he has with only a little help from his rich family.
D: You're doing an excellent job considering you only have a GED...
Innuendo: When you imply something negative about a person or organization without explicitly stating it.
3b
A: Obviously, critical thinking 102 is the most important class you'll ever take.
B: It goes without saying that Obamacare is a complete failure.
Truth Surrogates: Words like "obviously", "clearly", "it goes without saying", etc... are used in place of actually supplying supporting reasons for the claim.
3c
3.h
A: Women vs Men with Math
B: Oh! You're a philosopher? You must be charming and witty.
Stereotyping: An unwarranted conclusion or generalization about an entire group of people and/or to judge someone not as an individual but as a part of a group whose members are thought to be alike.
MAIN POINT FOR CRITICAL THINKING
1. Get through the lesson quickly. Most of the concepts you will already be familiar with.
2. Drink coffee and eat donuts.
2. Work on the midterms.
Part 1
Examples:
A: You may have already won a brand new car!
B: Some doctors recommend ginko biloba for improved cognitive function.
C: This is perhaps the best diet product ever made!
D: Up to 2/3rds of all people who used Chemain de Fer face cream said they thought they looked younger after just one week!
E: Our best deal yet! This Saturday get up to 50% off!
F: This ancient Chinese medicinal tea relieves joint soreness in up to 60% of people who tried it!
G: As many as 10 students will receive A's in Ami's class!
H: Buy Ami's Non-GMO All-Natural Organic Dirt! Just one application and your fat/acne will seem to melt away/disappear over night!
I: This drink from acai berries from the ancient Amazon rainforest will virtually change your health for the better in a matter of days!
J. BGSU named one of America's best colleges!!!!!http://www.bgsu.edu/news/2016/09/bgsu-named-one-of-americas-best-colleges.html
Weasel words are words that are used to appear to make a strong claim but avoid outright lying. Common weasel words are: "up to x percent/x number", "some", "as many as", "reportedly", "virtually", "many", "seems", "perhaps".
Part 2:
Examples:
A: Happiness Double Joy paper towels are 25% more absorbent!
B:
C: It's Christmas in July! All Dell computers sold below suggested retail price!
D: Phone company X lets you call anywhere cheaper. Just 5 cents per minute compared to Phone Company Y, which charges 10 cents a minute (Lewis Vaughn).
Misleading Comparisons: Often comparisons can mislead by omitting what something is being compared to (E.g., A&C), comparing apples to oranges (E.g., D), or puffery (E.g., B) (legal term in advertising law for hype that few people would take seriously).
Part 3a
A: I'm fairly certain my students with laptops aren't on facebook at this particular moment.
B: I think it's pretty safe to assume Mr. X is a responsible teacher. He hasn't done any drugs in quite a while.
C: Are you talking about politician X? I think that it's great that he's gone as far as he has with only a little help from his rich family.
D: You're doing an excellent job considering you only have a GED...
Innuendo: When you imply something negative about a person or organization without explicitly stating it.
3b
A: Obviously, critical thinking 102 is the most important class you'll ever take.
B: It goes without saying that Obamacare is a complete failure.
Truth Surrogates: Words like "obviously", "clearly", "it goes without saying", etc... are used in place of actually supplying supporting reasons for the claim.
3c
- Eg. collateral damage, detainees, passed away, senior citizen, downsizing, smart bomb, “put to sleep, pre-emptive defensive strike, freedom fighters, .
- Why does it matter? Usually a claim is being made but it obscures important information to the issue.
DEF: A euphemism substitutes mild and indirect ways of speaking for ways that might seem blunt, harsh, or impolite for social context. Often to neutralize emotional content.
- There are legitimate and illegitimate uses:
- legitimate: When the word is not part of an argument or when the euphemism is more appropriate for social context (he passed away, dog was put down, I had my dog's anal glands expelled...)
- the usage is illegitimate if the meaning of a term or phrase in an argument is obscuring important information.
3d
- E.g., Bleeding-heart liberal, heartless conservative, shopping-cart Christian, activist judge,
- hysterical tone (used vs women), puritanical zealotry (vs. Religious), bigoted, fear-mongering campaign, perverse logic
Def: A dysphemism substitutes emotionally neutral words for emotionally evocative words.
3e
- “Abortion is the murder of an unborn child.”
- “A conservative is someone who believes all problems can be solved with
- more guns and more Jesus.”
- "A liberal is someone who thinks all problems can be solved by more government."
Rhetorical Definition (vs Lexical Definition): When you define a term in such a way as to manipulate (often with emotional language) how the audience feels about a concept. It is a way of rigging the terms of the debate in the arguer's favor. Often used in conjunction with poisoning the well/genetic fallacy/ad hominem/circumstantial ad hominem.
The Lexical Definition is the definition of a term/concept as it is most commonly used by users of the language.
3f
3f
Stereotyping: An unwarrented conclusion or generalization about an entire group of people.
3g
3g
A: Fox news is fair and balanced? Ha! It's about as balanced as the leaning tower of Pisa.
B: MSNB's slogan is "lean forward." More like "lean left"!
C:
Ridicule: The use of derision, sarcasm, laughter, or mockery to disparage a person or idea (Lewis Vaughn).
3.h
A: Women vs Men with Math
B: Oh! You're a philosopher? You must be charming and witty.
Stereotyping: An unwarranted conclusion or generalization about an entire group of people and/or to judge someone not as an individual but as a part of a group whose members are thought to be alike.
MAIN POINT FOR CRITICAL THINKING
Why do rhetorical devices matter?
- Because there is a claim being asserted instead of providing an argument. If X is so q, then give an argument to show this. Making the assertion isn't an argument.
Monday, March 10, 2014
Lecture 8A: Vagueness, AmbiguityX2, Fallacy of Equivocation, Fallacy of Composition, Fallacy of Division
Business and Warm Up
1. What are the most common arguments you hear to justify eating meat (from factory farmed animals)? How do they fail? What arguments did you come up with?
2. Any questions about the midterm project? Be sure to justify your answers--that's the whole point of the course.
3. Wednesday will be a "coffee shop" class.
Lecture 8A
In your teams, look at the sample claims. Identify the common theme. When your team has figured it out, write it on a piece of paper, jump up and down while saying "I'm a monkey! I'm a monkey!" and submit it to me. To complete the challenge you must also create your own example.
Part 1:
A: "Everything is love...maaaaaaaan"
B: Happiness is a continuation of happenings which are not resisted.
--Depak Chopra
C: To think is to practice brain chemistry.
--Depak Chopra
D: A person is a pattern of behavior, of a larger awareness.
--Depak Chopra
E: New and Improved Formula!
--Every advertisement ever
F: Product X "boosts your immune system and help support and maintain a healthy lifestyle."
--Every supplement ever.
G. Am I allowed to pick mushrooms?
From Oak Openings Rules and Regulations: Within the parks and public lands of the Park District, no person shall without lawful authority or privileged to do so cut down, destroy, remove, girdle, or injure a vine, bush, shrub, sapling, tree, or crop standing or growing therein, or sever, injure or destroy a product standing or growing therein or other thing attached thereto; nor shall any tree, flower, shrub, or other vegetation, or fruit or seed thereof, or soil, or rock, or mineral be removed, injured or damaged; nor shall any form of wildlife, except fish, be injured, damaged or removed without specific written permission from the Director or his/her agents. (MM)*
Vagueness: A definition is vague it has no specific meaning for the intended audience.
Part 2A:
To win this round you must rewrite the sentences 2 reflect the (at least) 2 possible meanings. First group to scream like monkeys and hand in their answers wins.
A: I like her more than you.
B: People actually eat more sushi in America than in Japan.
C: He shot the elephant in his pajamas.
D: Ami said on Monday he'd give an exam.
E. Feel free to respond to my comments or disagree with me. (An actual message sent by a friend inviting me to follow him on twitter)
Part 2B:
A: He was found by his friend.
B: Apparently my parking is quite good. Someone left an official note on my window that said "parking fine."
C: Vitamin E is good for aging people.
D: Sign: Watch repairs here.
E. I'm not a big banana pancake fan.
Terms are ambiguous when they have more than one plausible interpretation. ("Ambi" means "two"). Ambiguity comes in two flavours: syntactic and semantic. Syntactic ambiguity (also called "amphiboly") is when the sentence structure offers more than one plausible meaning. Semantic ambiguity is when a word can have two possible meanings. Generally, context sorts outs semantic ambiguity (but not always).
Part 3:
A: Person 1: Everything in life happens for a reason...maaaaaaaaaaaaaaaan.
Person 2: That's ridiculous. What's the reason for my moving my finger right now?
Person 1: Because you had the thought "move your finger" which caused the nerves leading to your finger to fire in succession, culminating in the movement of your finger...duh.
B: Science has discovered many laws of nature. This surely constitutes proof that there is a God, for wherever there are laws, there must be a lawgiver. Consequentially, God must exist as the great lawgiver of the universe.
C: Since, as scientists tell us, energy neither comes into being nor goes out of being, there should be no energy crisis.
Fallacy of Equivocation: The fallacy of equivocation is when a key term in the argument isn't used with a consistent meaning throughout the premises and/or conclusion. In other words, a term might be used differently between premises or between the premises and the conclusion. Test hint: This is Ami's favorite fallacy.
Part 4:
A: Everything in the universe could not have created itself, therefore the universe also could not have created itself.
--William Paley's Teleological Argument
B: If everyone pursues their own best interest, societies best interests will also be served.
--Libertarianism
C: Since everyone cares about their own individual happiness, they will also care about the aggregate happiness of society.
--J.S. Mill in Utilitarianism
D: This dinner is going to taste delicious: Every ingredient it's made from is delicious.
--My mom.
E: Every person in the class was born to a mother therefore this class was born to a mother.
F: He/She's got every quality I like in a person. I'm sure we'll get along.
Fallacy of Composition: (P1) Since the parts P, Q, R which make up X have property(ies) a, b, c, then (P2) X must also have properties a, b, c.
Part 5
A: This food tastes awful. You must have used horrible ingredients.
B: The Seattle Seahawks were the best team in the NFL this year. They must have the best players.
C: The science man says there's supposed to be global warming, so why is it so cold in the D?
Fallacy of Division: (P1) Since some whole has property a, then (P2) it's parts (X, Y, Z) must also have property a.
No Homework...Bring me beautiful take-home midterms.
1. What are the most common arguments you hear to justify eating meat (from factory farmed animals)? How do they fail? What arguments did you come up with?
2. Any questions about the midterm project? Be sure to justify your answers--that's the whole point of the course.
3. Wednesday will be a "coffee shop" class.
Lecture 8A
In your teams, look at the sample claims. Identify the common theme. When your team has figured it out, write it on a piece of paper, jump up and down while saying "I'm a monkey! I'm a monkey!" and submit it to me. To complete the challenge you must also create your own example.
Part 1:
A: "Everything is love...maaaaaaaan"
B: Happiness is a continuation of happenings which are not resisted.
--Depak Chopra
C: To think is to practice brain chemistry.
--Depak Chopra
D: A person is a pattern of behavior, of a larger awareness.
--Depak Chopra
E: New and Improved Formula!
--Every advertisement ever
F: Product X "boosts your immune system and help support and maintain a healthy lifestyle."
--Every supplement ever.
G. Am I allowed to pick mushrooms?
From Oak Openings Rules and Regulations: Within the parks and public lands of the Park District, no person shall without lawful authority or privileged to do so cut down, destroy, remove, girdle, or injure a vine, bush, shrub, sapling, tree, or crop standing or growing therein, or sever, injure or destroy a product standing or growing therein or other thing attached thereto; nor shall any tree, flower, shrub, or other vegetation, or fruit or seed thereof, or soil, or rock, or mineral be removed, injured or damaged; nor shall any form of wildlife, except fish, be injured, damaged or removed without specific written permission from the Director or his/her agents. (MM)*
Vagueness: A definition is vague it has no specific meaning for the intended audience.
Part 2A:
To win this round you must rewrite the sentences 2 reflect the (at least) 2 possible meanings. First group to scream like monkeys and hand in their answers wins.
A: I like her more than you.
B: People actually eat more sushi in America than in Japan.
C: He shot the elephant in his pajamas.
D: Ami said on Monday he'd give an exam.
E. Feel free to respond to my comments or disagree with me. (An actual message sent by a friend inviting me to follow him on twitter)
Part 2B:
A: He was found by his friend.
B: Apparently my parking is quite good. Someone left an official note on my window that said "parking fine."
C: Vitamin E is good for aging people.
D: Sign: Watch repairs here.
E. I'm not a big banana pancake fan.
Terms are ambiguous when they have more than one plausible interpretation. ("Ambi" means "two"). Ambiguity comes in two flavours: syntactic and semantic. Syntactic ambiguity (also called "amphiboly") is when the sentence structure offers more than one plausible meaning. Semantic ambiguity is when a word can have two possible meanings. Generally, context sorts outs semantic ambiguity (but not always).
Part 3:
A: Person 1: Everything in life happens for a reason...maaaaaaaaaaaaaaaan.
Person 2: That's ridiculous. What's the reason for my moving my finger right now?
Person 1: Because you had the thought "move your finger" which caused the nerves leading to your finger to fire in succession, culminating in the movement of your finger...duh.
B: Science has discovered many laws of nature. This surely constitutes proof that there is a God, for wherever there are laws, there must be a lawgiver. Consequentially, God must exist as the great lawgiver of the universe.
C: Since, as scientists tell us, energy neither comes into being nor goes out of being, there should be no energy crisis.
Fallacy of Equivocation: The fallacy of equivocation is when a key term in the argument isn't used with a consistent meaning throughout the premises and/or conclusion. In other words, a term might be used differently between premises or between the premises and the conclusion. Test hint: This is Ami's favorite fallacy.
Part 4:
A: Everything in the universe could not have created itself, therefore the universe also could not have created itself.
--William Paley's Teleological Argument
B: If everyone pursues their own best interest, societies best interests will also be served.
--Libertarianism
C: Since everyone cares about their own individual happiness, they will also care about the aggregate happiness of society.
--J.S. Mill in Utilitarianism
D: This dinner is going to taste delicious: Every ingredient it's made from is delicious.
--My mom.
E: Every person in the class was born to a mother therefore this class was born to a mother.
F: He/She's got every quality I like in a person. I'm sure we'll get along.
Fallacy of Composition: (P1) Since the parts P, Q, R which make up X have property(ies) a, b, c, then (P2) X must also have properties a, b, c.
Part 5
A: This food tastes awful. You must have used horrible ingredients.
B: The Seattle Seahawks were the best team in the NFL this year. They must have the best players.
C: The science man says there's supposed to be global warming, so why is it so cold in the D?
Fallacy of Division: (P1) Since some whole has property a, then (P2) it's parts (X, Y, Z) must also have property a.
No Homework...Bring me beautiful take-home midterms.
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