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- Changes in methods of reporting- Relevant differences in populations compared- Changes in Cost / Inflation (for investments: Taxes too!)Arguments and Rational BeliefThe fetus (or embryo) is alive.III. How to Lie with numbersExamplesMeaningless & Ill-Defined Numbers2b. Changes in methods of reporting2c. Relevant differences in populations compared2d. Changes in Cost / Inflation (for investments: Taxes too!)III.How to Lie with numbers (MAIN IDEAS)Problem of Hidden DataQuestion: How to solve the problem of hidden data?Answer: Controlled ExperimentsA AXConditional probabilityHow to do “Base Rate” problemsBishop’s PHIL 2100 Test #1: Study Guide• What’s an argument? o Argument- set of premises that gives us reason to believe a conclusion.• What’s the difference between a threat, a bribe and an argument? Give an example of each.o Threats motivate, arguments reasono Threat: Believe this or I’ll fire you!o Bribe: Believe this and I’ll give you lots of money!o Argument: Socrates is a man. All men are mortal. Socrates is mortal.• Argument identification.o Premises that support conclusion (whether True or False)o Good argument w/ false conclusion: If the moon is made of green cheese, then there are mice on the moon. The moon is made of green cheese. So, there are mice on the moon.o Bad argument with true premises & conclusion:. The sky is blue. The ocean is Blue. SO, The lake is Blue.o Bad argument with a true conclusion w/ premises that support the conclusion: Bishop is the best philosophy teacher. The best philosophy teacher is ALSO the best overall teacher. SO, the best overall teacher is Bishop.• What are the two ways to show that an argument is a bad argument? Give one example of an argument that fails in each of those ways (but not the other). o ONE: premises don’t support conclusion. TWO: Premises are false/NOT plausible. ONE: violets are blue. Roses are red. SO, God exists. TWO: Squares are Circles. If Squares = Circles, god exists. SO, god exists• Identify premises & conclusion.o Premises: Statements that LEAD to conclusiono Conclusion: FOLLOWS from premises• Argument analysis: You should ask: Are premises plausible? Do premises support conclusion?• Moral claims vs Descriptive claimso Moral Claims: Claims about our moral rights or duties or about what morally ought to be the case. Examples: I ought to help the poor. Abortion is morally.o Descriptive Claims: Claims about describes what is, was or will be. Descriptive claims can be true or false. Examples: Grass is pink. Bishop is 6 feet tall.o Moral claims : You should feed the hungaryo Prudential (Practical): You should wear a seat belto Legal: You should not J- walk• Ambiguous claims – Explaino Lead to difficulties, always distinguish between moral, legal and prudential claims.• Requirements on concrete moral arguments – Explaino For a concrete argument’s premises to support its conclusion, the argument will have to consist of both moral and descriptive premises.• Argument completion exercises• Argument that fails due to an ambiguous premise• Averages. The significance of “bell curve” distributions.o Mean: Arithmetic average of a range of values. [Add values & divide by # of values.]o Median: The midpoint value in a range of values. o Mode: The value that occurs most frequently in a range of values.• Be able to identify these different ways of “lying” with numbers: time frame; ambiguity, vagueness.o Time Frame: must account for inflation/ nominal vs. real dollarso Ambiguity: no comparison comparison (50% better!)o Vagueness: no fixed standards• No Comparison Comparison- no fixed standards to compare to• No fixed standard- - Base rates ignored (the # of relevant background events)- Changes in methods of reporting- Relevant differences in populations compared- Changes in Cost / Inflation (for investments: Taxes too!) Real $: Accounts for inflation Nominal $: Does NOT account for inflation• Be able to “lie” with numbers yourself in each of these ways.• Be able to explain the “lies.”• 2 Principles of Good Graphs: Proportionality & Labeling.o Proportionality- Incriments increase/decrease equallyo Labeling- Labels are represented equally/relevantly• Ways to mislead with bar graphs: Don’t start at zero; Irregular intervals (along vertical axis); Irregular intervals (along horizontal axis); Streching & scrunching graphs; Ignoring inflation.• Explain why picture graphs are always misleadingo Can be manipulated by differing scales/incriments• What determines the strength of an argument based on samples?o Random, Relative, Representative• What are some of the ways to get representative samples?o Pick random people, large amount, from relavant population• Explain bad argument based on unrepresentative sample with examples.o Half of the people at Reggae Music Festivals smoke pot. SO, half of all people smoke poto ¾ of the 4 people I asked around campus said they liked sardine pizza. SO, ¾ of FSU likes sardine pizza.• Examples: Be able to figure out whether an argument based on samples is a good one.• Be able to identify causal claims.o Causal Claim- X causes Y  must know what happens when X is present; and when X is absent* Diagnostic Reasoning- Backward causal reasoning. What X caused Y.• Understand what one must do in order to reason well about whether X causes Y.o Premises must support conclusion. Must be true/plausible. Control Group & significant deference • Examples: Be able to recognize when an instance of reasoning requires a control. Be able to describe what that control would be. o Any/every sample test needs to have a control group• Explain the problem of hidden data with examples.o control condition is absent. Control is NEEDED for accurate data. Ex: snickers causes zits. (need to find people NOT eating snickers too.)• Explain the 3 experimental designs, their strengths, their drawbacks.o Random: Start with the relevant population. Randomly choose a control & an experimental group. Introduce X to the experimental group. This is the best sort of study you can run. Problem: There can be serious moral and practical difficultieso Prospective: Pick out an experimental population w/ A, B, C, D, & X. Match it to a control population w/ A, B, C, D, but NOT X. This solves the “morality” problem with randomized studies. Problem of confounding factors: might also differ in other relevant respects. o

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FSU PHI 2100 - Test #1

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