In this video we are going to be dealing with common argument types. Now what I mean by that is not the weaken or strengthen kind of arguments, but the actual arguments these weaken, strengthen, etc., questions. That is, there are premises, followed by a conclusion. And inbetween, 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 is 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 a school employed an online learning course for its math classes. After using the course for one 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 it's 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 could 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 equals y.

And, finally, there are statistical exceptions. A recent survey determined that 40% of 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, and we don't even really know the size of that sample, we can't necessarily generalize those findings to the universal population.

So, samples represented 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, you know, 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 transcriptAnd these unstated assumptions fall into common categories, and that's what I call the common argument types. For instance, there is 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 a school employed an online learning course for its math classes. After using the course for one 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 it's 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 could 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 equals y.

And, finally, there are statistical exceptions. A recent survey determined that 40% of 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, and we don't even really know the size of that sample, we can't necessarily generalize those findings to the universal population.

So, samples represented 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, you know, 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.