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| | ===What are some of the opportunities and obstacles of trying to become a better critical thinking in the information age? === | | ===What are some of the opportunities and obstacles of trying to become a better critical thinking in the information age? === |
| − | The information age is overwhelming precisely because the growth of research knowledge gives us both more good reliable knowledge and more apparent or questionable findings and theories.
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| − | The information age gives thinkers today more information and more knowledge at their fingertips. However, having everything technological takes away the face-to-face conversations that are credible ways to become a better critical thinker.
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| | ===How has the progress of research and "knowledge work" contributed to and complicated the pursuit of truth?=== | | ===How has the progress of research and "knowledge work" contributed to and complicated the pursuit of truth?=== |
| − | The progress of research and "knowledge work" contributes to the pursuit of truth by creating more answers and basis of knowledge. There are also more ways to look at the question with more knowledge available. It complicates the pursuit of truth by creating more information but not necessarily reliable and credible information.
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| − | ===What is epistemology? What are some of the values and limitations of logic in becoming a better thinker? ===
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| − | Epistemology is the study of the origins and grounds for knowledge.
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| − | One of the values of logic is questioning the truth and therefore continually seeking knowledge. A limitation of logic would be it causes an inability to fully comprehend the emotional and moral aspects of the argument.
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| − | ===What does it mean to make your thought an object of thought? ===
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| − | An object of thought could be an issue, topic, or problem in your life. You are thinking about a topic, focused on that topic, until there is a resolution or something that that distracts you from that topic.
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| − | ===How can we describe thinking in ways that seem compatible with what we are learning from research on cognition and social conflict?===
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| − | Cognition is linked to your personal interpretation of how you understand a concept. It deals with how an individual understands the material. Social conflict deals with assessing the situation correctly and approachs the situation from different critical viewpoints.
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| − | ===What is a persona and how does your persona affect the quality of your deliberations? ===
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| − | The ''persona'' is essentially the facade or character one tends to adopt within reflective discussions. As noted in the text one could be a "gadfly," which would entail "listening carefully for much of the discussion but then interjecting a particularly insightful or disarming question or series of questions." In short, one's ''persona'' is the general personality one develops within the context of the discussion, which would pertain to one's behavior towards others, the matter at hand, and other aspects of the conversation. Other examples provided include someone that "holds forth," one that "holds the fort," or someone that is a "synthesizer." Depending on one's ''persona,'' the quality of the deliberation will vary. The individual ''personas'' allow for different levels of quality based on what one takes out of the discussion, in addition to other variables.
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| − | ===What are the three main critical thinking virtues?===
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| − | The three mail critical thinking virtues are sympathetic understanding, seeking knowledge, and inviting appraisal.
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| − | '''Sympathetic Understanding''' is to be able to enter into a sypathetic unsderstanding of the views being offered. This requires you to understand the meaning of what has been said and the perspective from which it makes sense.
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| − | '''Seeking Knowledge''' involves the following three questions about any view or argument: What is known? What is knowable? Who knows it?
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| − | '''Inviting Appraisal''' is your openness to appraisal, both to giving ctitical appraisals and to receiving them.
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| − | ==Chapter 2: Making Reflective Moves==
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| − | ===Understand and explicate terms and phrases such as: presumption, conversational implicature, burden of proof, rationales, claims, and logic chopping===
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| − | '''Presumptions:''' Claims that are generally taken to be true or implied to be true within the context (not from the actual content) of the deliberation. All communication must take certain truths (presumptions) for granted. We presume what we do not question. Presumptions are the claims/ideas that are not "in play" in the discussion. Presumptions are good things to question, but not too early. They are partly determined by the interests of parties to the conversation, but also by the social and historical context of the discussion. They are one of the "structures" underlying spoken/written communication that help organize our thinking and exchanges. Specific presumptions that are made depend on the speaker's personalities, cultural values, and historical context. They are "non-logical" and pragmatic aspects of communication
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| − | '''Conversational implicature:''' A nonlogical inference constituting part of what is conveyed by a speaker in making an utterance in a context, without being part of what is actually said in the utterance.
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| − | '''Burden of proof:''' An obligation that is normally attributed to a speaker/writer to provide credible/plausible reasons for his/her major claims. This shifts as more compelling reasons are offered for different claims. In a discussion, it depends upon what is already presumed. One way to shift this is to undermine/question a presumption.
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| − | '''Rationales:''' Any set of premises or reasons which imply a conclusion when the truth of those premises is somehow a basis for accepting or understanding the conclusion (when on thing is said for the basis of another)
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| − | '''Claim:''' A statement about the world that is either true or false. They become conclusions of a thought process when we think we have support for them.
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| − | ----VICTORIA MANKILLER
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| − | Formatting changes: Erik Boisen
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| − | ===Explain and be prepared to distinguish arguments from explanations.===
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| − | Arguments are reasons for believing a conclusion is true. If you are justifying a belief, the rationale is an argument.
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| − | Explanations are reasons for understanding how something came about. If you are trying to understand a fact, the rationale you offer is an explanation.
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| − | ----Victoria Mankiller
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| − | ===Review the key features of basic reconstructions and be prepared to give a basic reconstruction of a short argument.===
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| − | Reconstructions are how we represent rationales in a piece of speech/writing.
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| − | The key to good reconstructions is to follow these steps:
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| − | 1. Map or highlight the rationales so that you can distinguish arguments from explanations and premises from conclusions explicitly.
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| − | 2. Identify, distinguish, and separately reconstruct independent and dependent reasons for the main conclusion.
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| − | 3. Fill in missing pieces of the argument and edit out unnecessary details.
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| − | 4. Write out a summary of the rationales using good, clear, natural prose in a paragraph format. Be sure that your reconstruction makes it clear to the reader how various sub-arguments and explanations are organized in relationshiip to the overall point of the reasoning
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| − | ----Victoria Mankiller
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| | ===Review the principles of fair interpretation.=== | | ===Review the principles of fair interpretation.=== |
| − | ===1) The principles of clarity- A charitable interpretation is one in which the interpretor has tried to reconstruct the argument in it's best light by assuming that it's author was intelligent, well-intentioned, and in posession of at least some insight. It is more important to get the best representation of the argument rather than try to determine what the author is trying to say.
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| − | ===2) The principle of fidelity - Being "faithful" to the text requires that you scruptuously avoid making inferences about the author's meaning without good textual and contextual evidence. The best textual evidence is the author's own words and contextual evidence is often invaluable.
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| − | ===3) The principle of inclusion - Summarizing and putting together the reconstruction of the core of an argument and incorporating as much relevant information as possible into the argument. (OTHERS ADD TO THIS)
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| | ===Understand the difference between Conversational Interpretive Strategies and Rationale Engagement Strategies and be prepared to apply them in particular cases.=== | | ===Understand the difference between Conversational Interpretive Strategies and Rationale Engagement Strategies and be prepared to apply them in particular cases.=== |
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| − | 1. Conversational/Interpretive Strategies (CIS) = are based on an assesment we make about how to get involved based our judgment about the immediate context of the discussion.
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| − | 2. Rationale Engagement Strategies (RES) = are more narrowly focused on techniques for questioning, criticizing, or affirming an argument or explanation.
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| | ==Chapter 3: Sherlock’s Logic – Deductive and Inductive Inferences in Everyday Reflection== | | ==Chapter 3: Sherlock’s Logic – Deductive and Inductive Inferences in Everyday Reflection== |
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| | ===Within categorical logic, understand and apply terms such as: contradictories, contraries, subcontraries, subalterns.=== | | ===Within categorical logic, understand and apply terms such as: contradictories, contraries, subcontraries, subalterns.=== |
| − | CONTRADICTORIES-Contradictories have opposite truth values. If one is, the other must be false.
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| − | CONTRARIES-Contraries cannot both be true, but can both be false.
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| − | SUBCONTRARIES-Subcontraries cannot both be false, but can both be true.
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| − | SUBALTERNS-From true A or E proposition, you can infer a true I or O proposition.
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| | ===Within propositional logic, understand the main components of the logical system (claims or propositions, connectives, parentheses, brackets, and braces), the five main valid argument patterns, and how the valid argument patterns determine validity.=== | | ===Within propositional logic, understand the main components of the logical system (claims or propositions, connectives, parentheses, brackets, and braces), the five main valid argument patterns, and how the valid argument patterns determine validity.=== |
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| | ===Give examples of the wide range of types of explanatory questions.=== | | ===Give examples of the wide range of types of explanatory questions.=== |
| − | Why is Mars red?
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| − | Why do I feel the way I do?
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| − | Why do $2000 dollar bicycles weigh so little?
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| − | Why does toast seem to land butter side down when you drop it?
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| − | Why does the AIDS virus resist drug treatments?
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| − | Why are humans so aggressive?
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| − | Why did my friend treat me rudely the other day?
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| − | Why do humans laugh?
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| − | Why are some forms of poverty difficult to alleviate?
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| − | Sam Burke
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| | ===What are the main features of good explanations?=== | | ===What are the main features of good explanations?=== |
| − | Good explanations offer the most probable account of how something works, comes about, or fits together. While we sometimes talk as though explanations begin with the search for truth and end with discovery, it is more precise to say that explanations begin with doubt and end with the cessation of doubt
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| − | Sam Burke
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| − | First, good explanations have "internal coherence", which means that they make sense on their own terms. The parts of the explanation are plausibly related to each other without contradiction and plausibly related to the phenomenon being explained. Second, godd explanations have "external coherence", which means that they are consistent and compatible with our background knowledge of the world. There is an exception to this criterion, however, which is that new knowledge can sometimes call into question our background knowledge. Third, an explanation should be "testable", if it is at all possible. Fourth, good explanations also satisfy reasonable doubt. One thing is absolutlely certain about explanations- they are permanent and unavoidable part of our reflextive experience.
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| − | Alex Gatley
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| | ===What are the competing explanatory accounts of the redness of Mars?=== | | ===What are the competing explanatory accounts of the redness of Mars?=== |
| − | Why is Mars red? The truth of the matter is that Mars is red because it is covered in iron oxide, but this isn't very explanatory since we will immediately wonder, "Why is Mars covered in iron oxide?" One explanation is that iron oxide comes from water interacting with iron in the planet's rocks. This explanation points to clues such as long empty channels that may have formed from an abundance of water. Water suffused with iron deposits might have gotten into the hydro cycle, dispersing iron oxides in raindrops all over the planet. Another theory points to the fact that the very soil of Mars is made up of iron, suggesting that impacts from meteorites could be responsible for the amount of iron on the planet. The interesting thing about this theory is that it does not require the presence of water. Doing an experiment it was found that super oxide irons could be formed simply from the presence of the planet's atmosphere and UV radiation. Neither of these explanations are sufficiently strong to resolve doubt about the process that produced the iron oxides that make Mars red. Each has their own strength in explanation though, the first points to obvious physical clues while the second used the scientific method and conducted an experiment
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| − | Sam Burke
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| − | The short answer is that Mars is red because it is covered with iron oxide. However, the truth that truth is not very explanatory because we immediately want to know why it is covered. One explanation, which is now being challenged, is that the iron oxide comes from water interacting with iron in the planet’s rocks. This requires us to believe that Mars was covered in water early on in its formation. This may explain also the long empty channels that cover large areas of the planet. Water suffused with iron deposits might have gotten into the hydro cycle, dispersing iron oxides in raindrops all over the planet. This explanation suggests one mechanism to explain two things. The weakness is that we don’t really know if there was an abundance of water on Mars.A newer, alternative explanation is being offered by NASA scientist Albert Yen. Using data, Yen noticed that Martian soil is also full of iron, suggesting that impacts from meteorites could be responsible for the amount of iron on the planet. This explanation doesn’t require a presence of water. Together, the two explanations might be stronger than one.
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| − | Gwenna Carie
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| | ===What’s the difference between a “why” question and a “how” question?=== | | ===What’s the difference between a “why” question and a “how” question?=== |
| − | Science does not answer "why" questions, it only answers "how" questions. Science isn't trying to tell us why there are human beings on Earth, only how they might have gotten there. Both "why" and "how" questions are clearly explanatory from a psychological point of view, but scientists who insist on the distinction between scientific and non scientific explanation have a good point when they say that scientific explanations only have to do with "how" something works, came about, or fits together. Their worry is that we will make use of the concept of "telos" or purpose in a way that will prevent us from offering more compelling explanations that can only be offered if we focus on "how."
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| − | Sam Burke
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| | ===Do explanations need to connect to “ultimate purposes”? Be prepared to present both points of view.=== | | ===Do explanations need to connect to “ultimate purposes”? Be prepared to present both points of view.=== |
| − | Certainly there is no such need within the contemporary paradigm of the physical sciences. But many scientists are like most non scientists in wanting ultimate answers (final causes) to questions about the purpose of existence. They just didn't expect answers to that question to come from science. Explanations begin and end in wonder. We must first feel that something needs explaining to begin looking for the explanation and we continue until, for a variety of reasons, the desire diminishes. Everything from folklore stories about how the leopard got his spots to creation myths show the psychological demand for and power of the explanatory impulse in humans. We have sometimes failed to look for powerful explanations for things like disease and human suffering when explanations were possible. Consider the long standing explanation-stopper for poverty: "The poor you shall always have with you." Other times, we have looked for explanations when none were needed (as in coincidence or astrology). So, in addition to thinking carefully about each explanation that presents itself to us, we have to introspective about what might be motivating our desire for an explanation.
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| − | Sam Burke
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| | ===Can you see “causation”?=== | | ===Can you see “causation”?=== |
| − | We do not see causes in the world; we assign them to events on the basis of the way we believe those events to be related. Our beliefs are informed by experience, but really determined by observation. David Hume pointed out that when we attend closely to our experience, our senses give us evidence of the sequence of events, but not a perception of a "cause." It is only through habit of our experience, in which some perceptions reliably follow one another, that we assign the term "cause" to the connection between them.
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| − | Sam Burke
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| − | A famous 18th century philosopher, David Hume, pointed out that when we attend closely to our experience, our senses give us evidence of the sequence of events, but not a perception of a “cause.” It is only through the habit of our experience, in which some perceptions reliably follow one another, that we assign the term “cause” to the connection between them. We do not see causes in the world: we assign them to events on the basis of the way we believe those events to be related. Our beliefs are inform by experience, but really determined by observation. It is only something we can attribute to a certain kind of reliable connection between or among events.
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| − | Gwenna Carie
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| | ===What’s the difference between a necessary and sufficient condition?=== | | ===What’s the difference between a necessary and sufficient condition?=== |
| − | Necessary condition is one that has to be present in order for some consequent condition to occur. EX: The presence of acid in a solution is necessary for turning a litmus paper blue.
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| − | Sufficient condition is one that will help some event to come about. Your hand, the wind, or a tilted table are all sufficient antecedent conditions for the pencil to fall off the table.
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| − | Sam Burke
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| | ===Identify four of Mill’s methods and be prepared to explain each. === | | ===Identify four of Mill’s methods and be prepared to explain each. === |
| − | "Method of Agreement"- suppose you notice that whenever your friend Jared is around, there is an argument. Through numerous social situations, his presence is the common factor with which the consequent condition, an argument, always agrees. He's not a necessary condition for an argument, but he is a sufficient condition-arguments seem to occur whenever he is involved in a discussion. Used it testing drug effectiveness
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| − | "Method of Difference"- If you find two events related as antecedent and consequent, and then you remove a factor and notice a different consequent condition, that factor may be a causal factor. This is a fancy way of saying that the Jareds of the world often get identified as causal factors on the day that they are missing. Used in detecting a food allergy
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| − | "Method of Difference and Agreement"-When we test a drug on two groups of patients, selected for their likelihood of becoming or being ill. One group, the test group, receives the drug while the other group, the control group, gets a placebo. If the drug works is all of the test subjects who receive it, it becomes the common factor. If the placebo fails in the control group, it becomes the "difference" that helps prove the rule.
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| − | "Method of Residues"- helps us identify the relative weight of causes when there are multiple causes. This method comes into play when you already have a set of antecedent conditions that you believe are causally related to a consequent condition. For example, if groups of people are playing "tug of war" with a rope over a mud puddle, you could remove one of the players from one side and see how long the remaining players could stay out of the mud. That would be a measure of the residue causal effect of the remaining team members. Likewise , if you were taking several medications for a cold, you could stop on ad observe the residual effect of the others.
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| − | "Method of Concomitant Variation"- can be the most helpful, but is also most susceptible to misuse. If you increase or decrease the amount or influence of one or more factors (antecedent conditions) in a situation, and you observed a concomitant change in come consequent condition, you should consider the possibility that the antecedent and consequent conditions are related causally and vary together by some common measure. This can result in a direct variation between the conditions-as one or more factors goes up the other one goes up, or inverse variation- as factor or factors increase, some other condition decreases.
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| | ===What is “inverse” and “direct” variation?=== | | ===What is “inverse” and “direct” variation?=== |
| − | Direct variation between conditions- as one or more factors goes up the other one goes up.
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| − | Inverse variation- as factor or factors increase, some other condition decreases.
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| − | Sam Burke
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| | ===What is a “correlation coefficient”?=== | | ===What is a “correlation coefficient”?=== |
| − | A complex way of quantifying the strength of correlation in either single factor or multiple factor analysis. It is usually designated "r" and guarantees that r will always range between -1, in the case of a strong inverse variation, and +1, in the case of a strong direct variation. A value of r near 1 or -1 is necessary but not sufficient condition for inferring a causal relationship.
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| − | Statisticians have a very sophisticated way of quantifying the strength of correlation in either single factor or multiple factor analyses. They call it the “correlation coefficient.” The formula for calculating “r” in the case of “bivariate observations,” guarantees that r will always range between -1, in the case of a strong inverse variation, and +1, in the case of a strong direct variation. The degree of correlation between two phenomena does not by itself increase the strength of the inductive inference from correlation to cause.
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| − | Gwenna Carie
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| | ===What is the fallacy of “complex cause”? “common cause”?=== | | ===What is the fallacy of “complex cause”? “common cause”?=== |
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| − | Fallacy of "complex cause" - making a causal inference on the basis of one single factor when there are many factors involved.
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| − | Fallacy of "common cause" - claiming a causal relationship between two conditions (say A and B) because they are tightly correlated when in fact both have a common cause of a third condition (C).
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| − | Crystal Huff
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| | ==Chapter 5: “We Don’t Get Fooled Again” – Uses and Misuses of Numerical and Statistical Information== | | ==Chapter 5: “We Don’t Get Fooled Again” – Uses and Misuses of Numerical and Statistical Information== |
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| | ===What are some of the difficulties that people face when trying to use and discuss numeric and statistical information?=== | | ===What are some of the difficulties that people face when trying to use and discuss numeric and statistical information?=== |
| − | Data will sometimes turn out to be unreliable or its interpretation mistaken. We might remember the isolated piece of data we read in the newspaper or heard on tv, but then we have to decide whether to introduce it into a conversation. Bringing hard facts into a causal reflective conversation can make a person look like a "know it all" or can seem confrontational. In addition to this problem you typically have very little knowledge about how the survey was conducted and who produced the data. More recent data could contradict it.
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| − | The data sometimes turns out to be unreliable or its interpretation mistaken. Numeric and statistical data have a particularly powerful tendency to deceive or mislead. Some people are justifiably reluctant to bring so called “hard facts” into a casual reflective conversation because it may seem too confrontational.People shy away from numbers because they are not confident enough about the underlying mathematical knowledge needed to interpret them completely.
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| − | Gwenna Carie
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| | ===What is “innumeracy”?=== | | ===What is “innumeracy”?=== |
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| − | Innumeracy is the inability to think about numerical information. Many people are not confident enough about the underlying math used and the knowledge of that math needed to interpret the statistical informational competently.
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| − | Crystal Huff
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| | ===Idenify the main kinds of problems understanding and thinking about numeric and statistical information, including problems of context, large numbers, compounding, linearity, baseline, surveys and sampling, odds, probability, correlation, and cause. === | | ===Idenify the main kinds of problems understanding and thinking about numeric and statistical information, including problems of context, large numbers, compounding, linearity, baseline, surveys and sampling, odds, probability, correlation, and cause. === |
| − | [[Problems of context-]]The context of the numbers matters to our assessment of the information. Every student or admissions counselor knows that context matters tremendously when looking at grade reports. The ease or severity of a test must be considered when interpreting grades from a student, group, or a whole school. Context also come into play when we are looking at financial information, especially if it involves large numbers.
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| − | [[Large numbers-]] People have a particularly hard time being critical about numeric information when the quantities are either very big or very small.
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| − | [[Compounding –]] an operation that makes a numeric relationship non-linear. It occurs whenever you add to some amount by a percentage at intervals over time.
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| − | [[Linearity-]] People also get confused about percentages when they misunderstand the difference between linear and non-linear phenomena. In a linear relationship, every increment of change in x corresponds to a proportional change in y. What makes it a linear relationship is that the increases or decreases in the variables make a constant proportion (1 to 1). In a non-linear relationship, this proportion changes at different points on the line.
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| − | [[Baseline-]]One of the best ways to avoid deception about quantitative comparisons, especially those involving trends, is to establish a “baseline” for comparison. A baseline is a rule, often a definition of a measure, which serves as the standard for comparisons with respect to that measure. I.E. wages – while the nominal wage has been constant, the real wage probably declined due to inflation.
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| − | [[Surveys and sampling-]] The central concept of good surveys is representative sampling. The problem with sampling is unrepresentative sampling, as demonstrated with the 1936 poll of Alf Landon and Franklin Roosevelt. The key to avoiding this kind of problem is to make sure that the sample surveyed is representative of the whole group. A sample is representative is every relevant difference in the sample has an equal chance of appearing in the total population. When reading a poll result, you should be able to learn how many people were questioned, over what period of time, and by what means.
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| − | On very controversial topics, the wording of the question can determine whether a majority comes out in favor of the question or against it. Sometimes a poll result depends upon how many choices of response are provided, how options are lumped together, and whether terms are used that are “loaded” or simply perceived differently by the respondents to the poll. People also systematically under-report some kinds of information (i.e. criminal behavior) and over report issues such as voting.
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| − | More subtle measurement errors occur when people are polled about things they do not understand of when the answers to one question affects the answer to the next.
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| − | Survey data can be influenced by public trends also, such as more women discussing eating disorders with their doctors so it might seem like there was surge in new cases of illness when in fact there was just an increase in the willingness to report it.
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| − | Another technical problem to consider in public opinion polling is “margin of error.” Good quality polls report their “margin of error.”
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| − | [[Odds-]] People have some interesting and stubborn biases when it comes to weighing objective information about probabilities against their deepest convictions. People know that the odds of dying in an automobile accident are far greater than the odds of dying in a commercial airplane, but they nonetheless fear the latter a great deal more than the former.
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| − | [[Probability-]] The difficulty that most people have understanding probability partly explains the success of Las Vegas casinos and lotteries, though many people seem to find it entertaining to watch probabilities play themselves out as their money disappears. In all casino games, the odds are against you, sometimes by a small amount and sometimes by a huge margin. If there is enough charm and mystique, or just enough blinking lights, people will play.
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| − | It is also probably a good thing people don’t understand probabilities accurately or else they would go into the state of “depressive realism” that suggests that people who are depressed have a more accurate assessment of a wide range of probabilities. Depressed people are accurate judges of how much skill they have, whereas happy people think they are much more skillful that others perceive them to be.
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| − | Another problem is that a hopeful person is someone who sees positive possibilities in the future. Their general outlook on life conditions their subjective assessment of probabilities.
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| − | One of the most common ways that people become confused about probability is by failing to take into account the size of the sample space (the number of ways that some event can occur). Given a very large number of events many seemingly unlikely things are bound to happen.
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| − | [[Correlation-?]]
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| − | [[Cause-?]]
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| − | Gwenna Carie
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| | ===Why is it that wasting a billion dollars might not be such a big deal for the Federal Government?=== | | ===Why is it that wasting a billion dollars might not be such a big deal for the Federal Government?=== |
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| − | Many people are convinced that a significant percentage of government tax revenue is simply wasted. To think about this, let’s take the 2001 federal budget of 2 trillion (2 x 10^12) and burn a billion dollars. It is not wasteful considering the baseline. The calculation is simple: 1 x 10^9 / 2 x 10^12 = .5 x 10^-3, or .05%. If the federal government only wasted a billion dollars of your money, it would be 99.95% efficient.
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| − | Gwenna Carie
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| | ===Identify and explain these terms: representative sample, depressive realism, sample space, sampling error, the law of large numbers, gambler’s fallacy, bell curve, multiple regression analysis, === | | ===Identify and explain these terms: representative sample, depressive realism, sample space, sampling error, the law of large numbers, gambler’s fallacy, bell curve, multiple regression analysis, === |
| − | Representative sample: A sample is representative if every relevant difference in the sample has an equal chance of appearing in the total population. A relevant difference is one which would affect the opinion being measured). The key to avoiding an unrepresentative sampling is to make sure that the sample surveyed is representative of the whole group.
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| − | Depressive Realism: Hypothesis that suggests that people who are depressed have a more accurate assessment of a wide range of probabilities. While evidence is not conclusive, survey data suggests that people routinely overestimate their abilities and underestimate the chances of negative outcomes for themselves. As Martin Seligman writes, "Depressed people are accurate judges of how much skill they have, whereas happy people think they are much more skillful than others judge them to be." Seligman goes on to qualify these findings because he does not want people to infer that being happy is somehow a bad or disadvantaged strategy. In fact, it may be that optimism and hopefullness of the happy person is very adaptive.
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| − | Sample Space: The total number of possible different outcomes in the "classical view" of probability. Given a large number of events, many seemingly unlikely events are bound to occur. Predictive dreams are an example of this when a person dreams of some event and then it happens a few days later.
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| − | Alex Gatley
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| − | Law of Large Numbers: Tells you that the opposite outcome is likely to happen because the previous outcomes have been one. It is false as the example of flipping a coin states because even if you flip heads 5 times in a row, the likelihood of getting tails is still 50%. Don't be swayed by the one-sidedness this can bring about. "Repeated, independent trials with the same probability 'p' of success in each trial, the percentage of successes is increasingly likely to be close to the chance of success as the number of trials increase."
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| − | Gambler's Fallacy: Use Law of Large Numbers to tell one that the likelihood of an event happening will increase. Flipping a coin is a good example because even though it appears you have a better chance to flip tails after 5 heads in a row, you actually don't. These events are independent which is why the probability is always 50% of getting heads (or tails).
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| − | Bell Curve: Function that fits any randomly selected sample of observations. Also know as the Normal Distribution curve. Observations will cluster at average value.
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| − | Multiple Regression Analysis: Measures the weight of multiple independent variables on some dependent variable. Used to see the effect more than 1 factor has on the regression line.
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| − | Erik Boisen
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| − | '''NEED:''' Sampling Error
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| | ===What is the Sports Illustrated jinx? Do you think it’s real? Why or why not?=== | | ===What is the Sports Illustrated jinx? Do you think it’s real? Why or why not?=== |
| − | The idea that if an athlete is on the cover of Sports Illustrated that athlete will either have a bad year or get injured in the current or upcoming season (if off-season). It has happened to 913 of the 2,456 persons featured on the cover of SI, which could lean one toward believing in it. We use past occurrences as our data to analyze the situation, but even it cannot predict an athlete's performance or health. Many use the idea that these athletes have outperformed the averages in their field of skill so they are located on the right hand side of the bell curve.
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| − | Erik Boisen
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| | ==Chapter 6 – The Way Up is the Way Down – Thinking Through Complexity.== | | ==Chapter 6 – The Way Up is the Way Down – Thinking Through Complexity.== |
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| | ===Give an example of how sciences simplify things to build models and be prepared to say something about the limits of a simple model of causality.=== | | ===Give an example of how sciences simplify things to build models and be prepared to say something about the limits of a simple model of causality.=== |
| − | An example of how sciences simplify things is if you want to study the causal variables involved in the movement of a baseball from the pitcher to the catcher, you can simplify your job by assuming that the baseball is a perfect sphere, that it is “thrown” by a precise force at a known angle, and that it is moving through a vacuum. By this you can predict with great accuracy the entire path of the theoretical baseball. The limits to a simple model of causality are that things usually are more complex. There are patterns in a complex phenomena. The exact path of the baseball thrown by a real human being is unique and unknowable. Simplifying models does not take into account reality, but more theoretical situations.
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| − | Carson Van Valkenburg
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| | ===What is network theory?=== | | ===What is network theory?=== |
| − | Also called a graph theory. It deals with the idea of “six degrees of separation. That any one of us is related to any other human being by an average of six intermediate steps. We are all connected within a network of links and nodes.
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| − | Carson Van Valkenburg
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| | ===What does “six degrees of separation” mean?=== | | ===What does “six degrees of separation” mean?=== |
| − | Everyone is connected to every other person on Earth through 6 other people. For example, Joe knows Bob(1) who knows Sue(2) who knows Pat(3) who knows Bill(4) who knows Mary(5) who knows George(6). Ideally, George would then know Joe to complete the circle, but in good networking situations many of these people know each other also.
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| | ===Complex systems: inerrelatedness, 1965 New York City power outage (sig. of), coupling/decoupling.=== | | ===Complex systems: inerrelatedness, 1965 New York City power outage (sig. of), coupling/decoupling.=== |
| − | Interrelatedness is the main characteristic of a complex system. The interrelatedness of parts or components- the nodes on a network representation of the systems. Roughly, the more ways that more nodes can affect other nodes, the more interrelated a system is. The 1965 New York City power outage was significant because it showed the value and cost of interrelatedness. Using a modern power grid, electrical power can be managed more efficiently by channeling power to parts of the gird that need it at different times of the day. But it also means that everyone in the grid is related in complex ways to everyone else in the grid. A failure triggered the disconnection of a transmission line, which is what was supposed to happen. But in this case, the unintended consequence was that nearby parts of the system experienced a transmission overload, triggering a cascade of “disconnect” responses from switches throughout the grid.
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| − | Coupling: systems can be more tightly or loosely coupled depending upon the degree to which a failure in one part of the system affects another. Decoupling is when you want that failure to be contained. When you are alone your behaviors are “decoupled” from the view and judgment of others.
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| | ===Be prepared to give your own examples of complex networks. === | | ===Be prepared to give your own examples of complex networks. === |
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| | ===Buffering, redundant systems, pos/neg feedback (examples of). === | | ===Buffering, redundant systems, pos/neg feedback (examples of). === |
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| − | Buffering is one way to manage tightily coupled systems. Buffering allows a person to create a tightly coupled system, but at the same time create containment if there is a failure within the actual system itself. Engineers of airplanes buffer the system by creating redundant and contingent systems. This means having a secondary system if one fails.
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| − | Positive and Negative feedback loops are really just direct and inverse correlations with an amplifying affect effect. Positive feedback takes the system out of equilibrium while negative feedback brings it back to equilibrium.
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| − | Redundant Systems?
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| − | Ryan Keene
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| | ===The Beer Game (sig. of), Partner system for police (sig. of).=== | | ===The Beer Game (sig. of), Partner system for police (sig. of).=== |
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| | + | ===Political ideologies as clusters in a political network.=== |
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| − | The Beer Game- was created by MIT to altough students to investigate the causes of supply chain management. The game allows the user to adjust production to fit with the demand of the product. The user must deal with the result of feedback within the system.
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| − | Ryan Keene
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| − | ===Political ideologies as clusters in a political network.===
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| | ===Gottman’s work, significance, critical variables, Intransparency. === | | ===Gottman’s work, significance, critical variables, Intransparency. === |
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| − | Gottman studies the way couples interact with each other, particurally during arguments. He wires them up with electronic devices to monitor organs such as heart rate. He has been able to find critical variables to predict the outcomes of relationships. He can predict whether a couple will still be married in fifteen years. There are two different states positive setiment override and negative setiment override. Every couple fights and bickers but its how you respond to those that determines what state they are in.
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| − | Ryan Keene
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| | ===Dorner’s work. Characteristics of good managers=== | | ===Dorner’s work. Characteristics of good managers=== |
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| − | In short, good managers differed from bad ones by making more decisions overall in order to achieve their goal. They also relaized that they might need to fiz other problems in order to find a solutioin to another. Good managers also manage situations and not processes. They understand that sometimes you can't just say things are truths, you first have to make them hypotheses.
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| − | Ryan Keene
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| | ===Chaos vs. Complexity. Characteristics of chaotic system. The weather. === | | ===Chaos vs. Complexity. Characteristics of chaotic system. The weather. === |
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| | ===Practical lessons from chaos theory for critical thinking.=== | | ===Practical lessons from chaos theory for critical thinking.=== |
| | ===Thin-slicing. Sig. of. === | | ===Thin-slicing. Sig. of. === |
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| | ===Intuition, sig. and problems of.=== | | ===Intuition, sig. and problems of.=== |
| | [[Category:Critical Thinking Study Question Collaboration]] | | [[Category:Critical Thinking Study Question Collaboration]] |
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| − | What do researchers on socio-linguistics and conflict tell us about the role of gender in deliberative communication?
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