# Analogical Arguments

An analogical argument is an inference from known similarities to a further similarity

##### Analogy
• Analogy is a similarity in some respects between things that are otherwise dissimilar. AH Dictionary
• LIKENESS, SIMILARITY, RESEMBLANCE, SIMILITUDE, ANALOGY mean agreement or correspondence in details. Merriam-Webster
##### Uses of Analogy
• Explaining an idea
• One-way encryption is like a sausage-grinder.
• HTML, CSS and javascript are like a house. HTML is the foundation and framing. CSS is decoration, like painting. Javascript consists of the doors to the outside.
• Suggesting a hypothesis or line of inquiry
• “The argument of Thomas Robert Malthus, the English economist, that populations tend to increase in numbers beyond the means of their subsistence suggested to Charles Darwin the evolutionary hypothesis of natural selection.” (Britannica)
• Arguing that something is true
• Sparks and lightning are alike in numerous ways. Sparks are electrical in nature. Therefore so is lightning.
##### Analogical Arguments
• An analogical argument, or argument by analogy, is an inference from known similarities to a further similarity.
##### Two Kinds of Analogical Arguments
###### Analogical Probability Arguments
• Form
1. A and B are alike in respects X, Y, Z
2. X, Y, and Z are relevant to whether a thing is W.
3. A is W
4. Therefore B is likely W
• Examples:
###### Analogical Deductive Arguments
• Form
• A and B are alike in all respects relevant to whether a thing is W.
• A is W
• Therefore B is W
• Examples:
##### Analogical Probability Examples
###### Benjamin Franklin’s Argument that Lightning is Electricity
• Letter to John Lining from Benjamin Franklin, November 7, 1749
• “Electrical fluid agrees with lightning in these particulars: 1. Giving light. 2. Color of the light. 3. Crooked direction. 4. Swift motion. 5. Being conducted by metals. 6. Crack or noise in exploding. 7. Subsisting in water or ice. 8. Rending bodies it passes through. 9. Destroying animals. 10. Melting metals. 11. Firing inflammable substances. 12. Sulphureous smell. The electric fluid is attracted by points. We do not know whether this property is in lightning. But since they agree in all the particulars wherein we can already compare them, is it not probable they agree likewise in this? Let the experiment be made.”
• Argument, Reconstructed
1. Lightning and sparks are alike in the following respects:
• 1. Giving light, 2. Color of the light, 3. Crooked direction, 4. Swift motion, 5. Being conducted by metals, 6. Crack or noise in exploding, 7. Subsisting in water or ice, 8. Rending bodies it passes through,  9. Destroying animals, 10. Melting metals, 11. Firing inflammable substances, 12. Sulphureous smell
2. A spark is electrical in nature
3. It’s therefore plausible that lightning is electrical in nature.
###### Comparative Market Analysis
• A comparative market analysis is used to estimate the sales price of a house based its similarity to nearby, recently sold houses.
• Argument
1. Nearby houses comparable to yours recently sold with an average sales price of \$250,000.
2. Therefore, the sales price of your house will be about \$250,000.
• Argument, Reconstructed
1. The subject house is similar to nearby, recently sold homes in regard to square footage, age, number of bedrooms, number of baths, garage spaces, location, size of lot, general condition, and amenities.
2. The average sales price of those houses is \$250,000.
3. The sales price of a house depends, more or less, on square footage, age, number of bedrooms, number of baths, garage spaces, location, size of lot, general condition, and amenities.
4. Therefore the sales price of the subject property will be about \$250,000.
###### Physical Model
1. A small model water turbine has been successfully tested under the same conditions under which full-size turbines are expected to operate.
2. Except for size, the model turbine is just like the full-size turbines.
3. Therefore, in all probability, the full-size turbines will operate without a problem.
##### Analogical Deductive Examples
###### Cheating is Wrong
1. There’s no morally relevant difference between cheating and surreptitiously changing your grade on the instructor’s spreadsheet.
3. Therefore, cheating is morally wrong.
###### Drawing CardsSimultaneously and Consecutively
1. Randomly drawing five cards simultaneously from a deck of 52 cards, as far as probabilities go, is just like randomly drawing five cards consecutively without replacement.
2. The probability of getting a flush (i.e. all cards having the same suit) in the latter case is 1/508.
3. Therefore the probability of getting a flush in the former case is also 1/508.
###### Judith Jarvis Thomson’s Famous Violinist
• Thomson’s Argument, Reconstructed:
1. In both scenarios:
• The subject is forced into having her body supply nutrients to another person
• If the subject stops the flow of nutrients within nine months the dependent person dies.
• The subject prefers not to have her body supply nutrients to the dependent person.
• The subject is able stop the flow of nutrients.
2. These are the only morally relevant facts.
3. It’s morally acceptable for the subject in violinist case to stop the nutrients
4. Therefore, it’s morally acceptable for the subject in the abortion case to stop the flow of nutrients.
###### Refutation by Logical Analogy
• An argument can be refuted by producing an obviously bad argument with the same form.
• Example
1. These arguments have the same form
• Abortion should be legal because abortions will be performed whether legal or not.
• Robbery should be legal because robberies will be performed whether legal or not.
2. The robbery argument is obviously bad.
3. Therefore the abortion argument is also bad.
###### Statistical Box Model
1. In the past five years 40 percent of qualified applicants for 100 new entry-level sales positions at the Widget Company have been women but only 20 percent of those hired were women.
2. This situation is just like the following box model:
• A box contains 10 slips of paper, 4 marked W, and 6 marked M.
• One hundred slips are selected at random, one at a time, their “gender” recorded, and replaced in the box.
3. The probability of selecting 20 or fewer slips marked W is 1/50,000.
4. Therefore, the probability that at most 20 women have been hired by chance is likewise 1/50,000.