# Discussion 2 (Inductive Inferences)

Your instructor will choose the discussion question and post it as the first post in the discussion forum. The requirements for the discussion this week are a minimum of four posts on four separate days, including responses to at least two classmates.. The total combined word count for all of your posts, counted together, should be at least 600 words. Answer all the questions in the prompt, and read any resources that are required to complete the discussion properly.

In order to satisfy the posting requirements for the week, complete your initial post by Day 3 (Thursday) and your other posts by Day 7 (Monday). We recommend that you get into the discussion early and spread out your posts over the course of the week. Reply to your classmates and instructor. Attempt to take the conversation further by examining their claims or arguments in more depth or responding to the posts that they make to you. Keep the discussion on target, and analyze things in as much detail as you can.

We have learned in Chapter 5 of our book that inductive inference is the most common kind of inference of all. It happens every day in each of our lives. This discussion will give each student a chance to create examples of common forms of inductive inference.

Prepare: To prepare to answer this prompt, take another look at Chapter 5 of our book, paying close attention to the names of the various forms of inductive inference. Take a look as well at the required resources from this week.

Reflect: Think about examples you have seen of each type of inductive inference in daily life. Consider the relative strength of such inferences in light of the methods of evaluation that you learned in the chapter.

Write: To answer the prompt, create or find one example each of three different types of inductive inference that we learned in Chapter 5. Clearly indicate as well which type of inductive inference it is. For each of your arguments, include an analysis of its degree of strength using the evaluative methods we learned in the chapter for that type of argument.

Guided Response: Respond to at least three of your classmates’ posts. In each case provide substantive thoughts about the strength of the inference. Mention as well what premises you think could be added to strengthen the inference or which might weaken it. How do you think that the argument could be improved?

Responce 1 Rochelle Rothstein

• Wednesday Jul 5 at 4:47pm

Appeals to authority, inductive generalizations, and statistical syllogisms are three common types of inductive inference. Appeals of authority means implying a truth because the source is an authority or subject matter expert. Some considerations to consider during analysis would be: Is the authority or subject matter expert a real, verified expert in the field? Do other subject matter experts in the field agree with the conclusion? Is the question relevant to the subject matter expert in their area of study?  Another consideration is if the subject matter expert in the field has a personal stake in the conclusion. If there is a personal interest, this could discredit the expert. A weak argument would include comments from non-reputable sources, such as articles provided in tabloids.  An example of a strong argument: “Abraham Lincoln served as president from April 4th, 1861 until April 15, 1865, my history professor said so”. Inductive generalizations draw conclusions based on poll results provided by the general population. My first thought with inductive generalizations is during election season when I receive phone calls from the candidates asking me questions about the election. Some considerations to consider are: Randomness – is the sampling used during collection general enough?  Sample size- is the sample used broadly sufficiently to provide robust data? The larger the sampling size, the better represented the results. Does the sampling include enough generalizations not to be predisposed? Data from a sample can be invalid unless feedback is provided from the entire population. A margin of error can be presented when providing data as a data range.  An example of a strong argument is: 67% of the population prefers Snickers with a margin of error of ±3%. Statistical Syllogisms is “using a general statistic about a subject to make an argument for a particular case and establishing a high degree of certainty about the truth of the conclusion. Statistical Syllogisms become valid categorical syllogisms when the percentage of, X, becomes 100% or 0%” (Foster, Zuniga & Postigo, 2015, para 5.2). To make a strong argument the samplings needs to encompass the entire population. An example of a strong argument is: 1% of high school males are on the volleyball team. Mason is a high school male. Therefore, Mason is not on the volleyball team. The argument is inductively strong. Using the general statistics, it would seem likely that Mason is not on the volleyball team. One way for a statistical syllogism to be weak is when the percentage is not close enough to 0% or 100%.   References:  Hardy, J., Foster, C., & Zúñiga y Postigo, G. (2015). With good reason: A guide to critical thinking [Electronic version]. Retrieved from https://content.ashford.edu/

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Appeals to authority, inductive generalizations, and statistical syllogisms are three common types of inductive inference.

Appeals of authority means implying a truth because the source is an authority or subject matter expert. Some considerations to consider during analysis would be: Is the authority or subject matter expert a real, verified expert in the field? Do other subject matter experts in the field agree with the conclusion? Is the question relevant to the subject matter expert in their area of study?  Another consideration is if the subject matter expert in the field has a personal stake in the conclusion. If there is a personal interest, this could discredit the expert. A weak argument would include comments from non-reputable sources, such as articles provided in tabloids.

An example of a strong argument: “Abraham Lincoln served as president from April 4th, 1861 until April 15, 1865, my history professor said so”.

Inductive generalizations draw conclusions based on poll results provided by the general population. My first thought with inductive generalizations is during election season when I receive phone calls from the candidates asking me questions about the election. Some considerations to consider are: Randomness – is the sampling used during collection general enough?  Sample size- is the sample used broadly sufficiently to provide robust data? The larger the sampling size, the better represented the results. Does the sampling include enough generalizations not to be predisposed? Data from a sample can be invalid unless feedback is provided from the entire population. A margin of error can be presented when providing data as a data range.

An example of a strong argument is: 67% of the population prefers Snickers with a margin of error of ±3%.

Statistical Syllogisms is “using a general statistic about a subject to make an argument for a particular case and establishing a high degree of certainty about the truth of the conclusion. Statistical Syllogisms become valid categorical syllogisms when the percentage of, X, becomes 100% or 0%” (Foster, Zuniga & Postigo, 2015, para 5.2). To make a strong argument the samplings needs to encompass the entire population.

An example of a strong argument is: 1% of high school males are on the volleyball team. Mason is a high school male. Therefore, Mason is not on the volleyball team.

The argument is inductively strong. Using the general statistics, it would seem likely that Mason is not on the volleyball team. One way for a statistical syllogism to be weak is when the percentage is not close enough to 0% or 100%.

References:

Hardy, J., Foster, C., & Zúñiga y Postigo, G. (2015). With good reason: A guide to critical thinking [Electronic version]. Retrieved from https://content.ashford.edu/

responce 2 Danny Liles

Wednesday Jul 5 at 5:34pm

Statistical Syllogism: P1 : 80% of steroid users develop serious health issues. P2: Androgen is a steroid. C: Therefore, Androgen users will develop serious health issues.      This argument looks strong, but it is flawed.  The argument assumes that androgen users will develop health issues just because it is a steroid.  The argument does not allow for rate of use or the amount.  It also does not take into account strength of androgen as opposed to other steroids.  It could be that androgen users fall into the 20% of steroid users that do not develop health issues. Argument from Authority: P1: Dr. Smith states that steroid users have an increased risk of heart disease. P2: Dr. Smith is the foremost authority in the study of heart health issues. C: Therefore, steroid users may develop heart disease.      In this argument, the conclusion is supported by the premises.  The authority figure supports the idea that the risk of steroid use is heart disease.  While the conclusion is not specifically true, the likelihood that many steroid users have a greater potential for heart disease is true based on the premises.  I believe this is a strong inductive argument. Argument from Analogy: P1: Blood doping use is similar to steroid use. P2: Steroid use has a high risk of serious health issues. C: Therefore, blood doping use also has a high risk for serious health issues.      This argument makes an analogy between steroid and blood doping use.  The conclusion is derived from the similarities in the two performance enhancing techniques.  The conclusion not necessarily true, but the likelihood of its truth is evident based on the premises.  I believe this is a strong inductive argument.      Please let me know if I can improve upon my arguments.

Statistical Syllogism:

P1 : 80% of steroid users develop serious health issues.

P2: Androgen is a steroid.

C: Therefore, Androgen users will develop serious health issues.

This argument looks strong, but it is flawed.  The argument assumes that androgen users will develop health issues just because it is a steroid.  The argument does not allow for rate of use or the amount.  It also does not take into account strength of androgen as opposed to other steroids.  It could be that androgen users fall into the 20% of steroid users that do not develop health issues.

Argument from Authority:

P1: Dr. Smith states that steroid users have an increased risk of heart disease.

P2: Dr. Smith is the foremost authority in the study of heart health issues.

C: Therefore, steroid users may develop heart disease.

In this argument, the conclusion is supported by the premises.  The authority figure supports the idea that the risk of steroid use is heart disease.  While the conclusion is not specifically true, the likelihood that many steroid users have a greater potential for heart disease is true based on the premises.  I believe this is a strong inductive argument.

Argument from Analogy:

P1: Blood doping use is similar to steroid use.

P2: Steroid use has a high risk of serious health issues.

C: Therefore, blood doping use also has a high risk for serious health issues.

This argument makes an analogy between steroid and blood doping use.  The conclusion is derived from the similarities in the two performance enhancing techniques.  The conclusion not necessarily true, but the likelihood of its truth is evident based on the premises.  I believe this is a strong inductive argument.

Please let me know if I can improve upon my arguments.

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