As a statistics major, I want add that the set of all people a correlation or causation relationship involves is called a population, while the selected participants in an experiment is called a sample. By having an experiment with a non-random sample of the population, there is a very high chance you'll get a sample unrepresentative of the population.
For instance, in the cruise ship example, 7Sage pointed out that the sample (those who self-selected to go on the cruise) is unrepresentative of the population (all people). If the causal hypothesis was instead "of people on the cruise ship, dramamine reduces seasickness" and the people who took dramamine vs. those who didn't were completely random, then the experiment would support it.
That self-selection/assignmentproblem underscored in the cruise argument, made the intended approach click.
Before reading on, here is my Theory and its application to causation related LR:
Attacking this argument I can now make a prediction on similar strengthen/weaken questions.
In this case, the self selected group of Dramamine takers are intentionally taking it for their high level of sea sickness. Hence exhibiting more symptoms
It would be helpful to also frame this walkthrough alongside the learning from previous classes about the 4 competing hypotheses and the ways to evaluate causal arguments (e.g., chronology, causal mechanisms etc). I think that's what is being discussed in the final paragraph ("alternative causes") and some of the comments in this Discussion thread have been helpful for this.
for an ideal experiment, it has to be: large sample (to exclude minor variations in characteristics but still have an amount representative of the population being measured), random sample (to avoid sampling bias/group that has a trait appearing more often than what is in the population being represented), random assortment (which is making sure that the only difference between the two groups which are the control and experimental is that one is given the experimental condition and one isn't) - i think these are the major things to keep in mind but there might be some smaller elements that they mentioned in the last lesson
"If you conclude that Tylenol makes people feverish, I'm going to slap you"
LOL I will be keeping this in mind as I study causation...I really like the examples too as I've been struggling with causal reasoning in drills and PTs
I take ibuprofen for cramps but still experience abdominal pain, therefore ibuprofen causes the pain WRONG - it may even alleviate it in some way bc the severity may have weakened, but the pain is still there, just minimized.
@m_k_a_a_a boo me on this if you want... but highkey, do not do this.
Not only can ChatGPT make mistakes in summarizing info, but taking information like these lessons that you might be unfamiliar with and breaking them down into something easier for you to understand is legit a skill the LSAT tests you on. There isn't going to be a ChatGPT-like resource for you on the actual exam, and not having this skill for a complex RC passage or LR stimulus will hurt you on the test. And hell more importantly, this skill shows up in plenty of situations in law school and beyond.
Developing this skill, as tough and frustrating as it is, and learning these lessons on your own is the smarter thing to do for your own skills and improvement than shortcutting your growth with ChatGPT
So glad to be paying for a study program that allows me to enjoy reading through the material. Mental breaks like this lesson was much needed, because idk about everyone else but this whole section had me go haywire
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48 comments
someone chose violence
why is he so sassy in this one
As a statistics major, I want add that the set of all people a correlation or causation relationship involves is called a population, while the selected participants in an experiment is called a sample. By having an experiment with a non-random sample of the population, there is a very high chance you'll get a sample unrepresentative of the population.
For instance, in the cruise ship example, 7Sage pointed out that the sample (those who self-selected to go on the cruise) is unrepresentative of the population (all people). If the causal hypothesis was instead "of people on the cruise ship, dramamine reduces seasickness" and the people who took dramamine vs. those who didn't were completely random, then the experiment would support it.
That self-selection/assignment problem underscored in the cruise argument, made the intended approach click.
Before reading on, here is my Theory and its application to causation related LR:
Attacking this argument I can now make a prediction on similar strengthen/weaken questions.
In this case, the self selected group of Dramamine takers are intentionally taking it for their high level of sea sickness. Hence exhibiting more symptoms
Love this lesson! It's hilarious. 😆
It would be helpful to also frame this walkthrough alongside the learning from previous classes about the 4 competing hypotheses and the ways to evaluate causal arguments (e.g., chronology, causal mechanisms etc). I think that's what is being discussed in the final paragraph ("alternative causes") and some of the comments in this Discussion thread have been helpful for this.
What is up with the random violence in the writing? You guys are abusive.
@VivienneTran im rocking with it tho
lol i love the evil genius vibes this lesson is giving
@Sunday_Blues13 Me too. lol
#feedback this makes so much sense but I would love videos on these topics!!
#feedback. why are these not on videos/ voiceovers?
#feedback this lesson would be a great voiceover video !!!
@alexapatafio yup
#feedback Pleeeeeeease give us functionality to highlight sections of text.
ugh I agree...it'd be sooo helpful bruh
@almostfamous yesss
so what are the basic principles of setting up an experiment, for it to random by picking out groups?
for an ideal experiment, it has to be: large sample (to exclude minor variations in characteristics but still have an amount representative of the population being measured), random sample (to avoid sampling bias/group that has a trait appearing more often than what is in the population being represented), random assortment (which is making sure that the only difference between the two groups which are the control and experimental is that one is given the experimental condition and one isn't) - i think these are the major things to keep in mind but there might be some smaller elements that they mentioned in the last lesson
Am I really gonna have the time to run a whole idea experiment on the test? no
This lesson was so easy to comprehend and the added lingo and character made it so much easier to read. Thanks!
"If you conclude that Tylenol makes people feverish, I'm going to slap you"
LOL I will be keeping this in mind as I study causation...I really like the examples too as I've been struggling with causal reasoning in drills and PTs
I take ibuprofen for cramps but still experience abdominal pain, therefore ibuprofen causes the pain WRONG - it may even alleviate it in some way bc the severity may have weakened, but the pain is still there, just minimized.
Tip: if these lessons confuse you, copy and paste the text into ChatGBT and ask them to simplify it and break it down in a way that makes sense.
@m_k_a_a_a boo me on this if you want... but highkey, do not do this.
Not only can ChatGPT make mistakes in summarizing info, but taking information like these lessons that you might be unfamiliar with and breaking them down into something easier for you to understand is legit a skill the LSAT tests you on. There isn't going to be a ChatGPT-like resource for you on the actual exam, and not having this skill for a complex RC passage or LR stimulus will hurt you on the test. And hell more importantly, this skill shows up in plenty of situations in law school and beyond.
Developing this skill, as tough and frustrating as it is, and learning these lessons on your own is the smarter thing to do for your own skills and improvement than shortcutting your growth with ChatGPT
So glad to be paying for a study program that allows me to enjoy reading through the material. Mental breaks like this lesson was much needed, because idk about everyone else but this whole section had me go haywire
so what is this a valid arguement or not??????
no.
Causal Arguments have nothing to do with valid or invalid: they're informal so naturally they are going to be invalid logically.
Best lesson of this section so far, pulled the last few lessons together nicely!
saying he's going to slap for thinking tylenol makes you feverish took me out!
Now we know what the last couple lessons are for lol
Could also be the Dramamine had no effect at all.
i love JY so much
a gen z or millennial is writing this, its too funny.