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/

Manage Discussion Entry

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.