Common Argument Types
In this video, we're gonna be dealing with common argument type. Now what I mean by that is not the weakened or strengthened kind of arguments but the actual arguments these weakened strengthen the central questions use. That is, there are premises followed by a conclusion. And in between so to speak, there are these unstated assumptions. And these unstated assumptions fall into common categories and that's what I call the common argument types.
For instance, there's cause and effect, statistical, and analogy. This type of reasoning accounts for many of the different types of assumptions that you will come across. Now, let's start with the cause and effect to give you a better sense of what I mean. Starting with school employs an online learning course for its math classes. After using the course for 1 year, teachers reported an increase in standardized test scores.
So the assumption here is that X caused Y. There's a clear cause and effect. X being the online learning course, Y being the increase in test scores. Now, the reason that there are holes here in this argument is because something else could cause Y, that is, Z could cause Y. What could Z be?
Well, perhaps the school hired all new teachers that year for its math class, or mostly new teachers. Therefore, the teachers themselves could be responsible for the increase in test scores. Not to say that the online learning course has no functionality but in general, there could be another factor.
So cause and effect is quite common when you come across the assumptions that an argument makes. Also common is an analogy, the analogy fallacy or mistake if you will and that fallacy is as follows, an online course was shown to be effective at Placer Elementary. Therefore, other schools in the district can expect similar results.
The analogy is that Placer Elementary is the same as the other schools, that is X = Y. So just because the online course was effective at this school, doesn't mean it will be effective in other schools. To be more specific, other schools can differ in a number of ways. Perhaps the student-teacher ratio, perhaps the implementation of such online courses or any courses.
And therefore, schools can fundamentally differ so we cannot come to the conclusion that X = Y. And finally, there are statistical assumptions. A recent survey determined that 40% of all high school students who used an online learning program reported an increase in score. Therefore, all high school students should use the program.
So a recent survey is just a sample of the whole, but based on that sample, we don't even really know the size of that sample. We can't necessarily generalize those findings to the universal population. So sample is representative of the entire population is the underlying assumption of a statistical argument. Now the key to this video is internalize these different arguments.
Because when you come across the prompts, you don't wanna just read get to the conclusion, and then go to the answer choices and throw the proverbial Hail Mary hoping for the answer choice. The key is that you anticipate the right answer by looking for these assumptions that fall into these predictable buckets.
Read full transcriptFor instance, there's cause and effect, statistical, and analogy. This type of reasoning accounts for many of the different types of assumptions that you will come across. Now, let's start with the cause and effect to give you a better sense of what I mean. Starting with school employs an online learning course for its math classes. After using the course for 1 year, teachers reported an increase in standardized test scores.
So the assumption here is that X caused Y. There's a clear cause and effect. X being the online learning course, Y being the increase in test scores. Now, the reason that there are holes here in this argument is because something else could cause Y, that is, Z could cause Y. What could Z be?
Well, perhaps the school hired all new teachers that year for its math class, or mostly new teachers. Therefore, the teachers themselves could be responsible for the increase in test scores. Not to say that the online learning course has no functionality but in general, there could be another factor.
So cause and effect is quite common when you come across the assumptions that an argument makes. Also common is an analogy, the analogy fallacy or mistake if you will and that fallacy is as follows, an online course was shown to be effective at Placer Elementary. Therefore, other schools in the district can expect similar results.
The analogy is that Placer Elementary is the same as the other schools, that is X = Y. So just because the online course was effective at this school, doesn't mean it will be effective in other schools. To be more specific, other schools can differ in a number of ways. Perhaps the student-teacher ratio, perhaps the implementation of such online courses or any courses.
And therefore, schools can fundamentally differ so we cannot come to the conclusion that X = Y. And finally, there are statistical assumptions. A recent survey determined that 40% of all high school students who used an online learning program reported an increase in score. Therefore, all high school students should use the program.
So a recent survey is just a sample of the whole, but based on that sample, we don't even really know the size of that sample. We can't necessarily generalize those findings to the universal population. So sample is representative of the entire population is the underlying assumption of a statistical argument. Now the key to this video is internalize these different arguments.
Because when you come across the prompts, you don't wanna just read get to the conclusion, and then go to the answer choices and throw the proverbial Hail Mary hoping for the answer choice. The key is that you anticipate the right answer by looking for these assumptions that fall into these predictable buckets.
