#feedback precisely. I think causation is an extremely important lesson to learn especially for LR, it would be great if we could get video lessons but also skill builder sections to identify correlation /= causation and practice identifying potential issues with the logic.
@AlexandraPolidora I think the idea is that when it comes to formal logic i.e. conditionals, then you cannot use outside information. On the other hand, for informal/ logic like causation and correlation, you can? I am assuming that when real practice shows up, then they will provide answer choices in which you have to decide whether they make sense in that particular context.
Definitely request more lessons like this where the main focus is just going through additional examples. Helps a lot!!! Especially for those of us who typically learn something through pattern recognition.
cheese causes more people to die by bedsheet entanglement because cheese makes tummy hurt and tummy hurt means restless sleep, which increases likelihood of becoming entangled in bedsheets and dying !
#help for the ice cream and drowning weather example, i instantly just thought it was a coincedence. how is that example different from the cheese consumption example? is this just an instinctual thing we have to know of how close or how far some things can be from causing or affecting another?
(A) per capita cheese consumption correlates with (B) the number of people who die by becoming entangled in their bedsheets
What if obesity is a (c) outside contributer to cheese consumption and being unable to untangle out of bed sheets thus leading to death?
This could happen all over the world, it could be tested, and direct evidence can be checked by looking at obesity rates. It might not be true, but isn't it a possibility?
I would love to know how this directly relates to questions we may see on the LSAT. I've read that you're not supposed to make any assumptions on the test, but it seems like all 4 types of hypotheses do just that?
a serial arsonist sends letters to fire departments telling them how many firefighters to send depending on the size of the fire they are going to start
Not me just sitting here trying to ponder if an increase in temperature could lead to more cheese consumption, heavy fat caloric intake to keep warm, and also people being in bed under their sheets more for warmth, wondering if C could indeed cause A and B separately lol...
My theory for the last scenario and general teaching suggestions
What if wealth is correlated with cheese consumption per capita, and with an elderly population, and an elderly population is correlated with bedsheet entanglement death?
If so then:
==ca==> cheese consumption per capita
Wealth of nation
==ca==> older population ==ca==> death by entanglement in bedsheets
I suppose because there is no =ca=> cheese =ca=> old =ca=> bedsheet death 'straight' link, there is no causation. There is correlation but both cheese and death are separately caused by the same thing 'wealth', but that does not mean one causes the other, just that both have the same cause, which if you go back far enough is true of everything. I think the above 'fork' presentation shows this well visually.
doesn't show for some reason (leftwards arrow, equals sign, 'co', equals sign, rightwards arrow, superficially looking like a weird biconditional indicating the tentative nature of the relationship, once causation is established, if established, the above suggested causation sign can be used).
91 comments
kinky ahh fromage
We need questions related to this section of the exam #feedback
#feedback precisely. I think causation is an extremely important lesson to learn especially for LR, it would be great if we could get video lessons but also skill builder sections to identify correlation /= causation and practice identifying potential issues with the logic.
#feedback it's a crime that you would put so many banger paragraphs in here and NOT have them voiced over with a video.
I still don't understand why we've been trained this whole time not to use outside knowledge and now this lesson is undoing all of that...
@AlexandraPolidora I think the idea is that when it comes to formal logic i.e. conditionals, then you cannot use outside information. On the other hand, for informal/ logic like causation and correlation, you can? I am assuming that when real practice shows up, then they will provide answer choices in which you have to decide whether they make sense in that particular context.
Can we all appreciate how funny and well-understanding the examples are?
The last example about bedsheets and cheese consumption was just too good to forget
Definitely request more lessons like this where the main focus is just going through additional examples. Helps a lot!!! Especially for those of us who typically learn something through pattern recognition.
@maryamvardehan YES THAT IS HELPFUL!! IVE RESORTED TO AI FOR ADDITIONAL EXAMPLES
#help I am so confused how this would apply to a real LSAT question... would love an example of some sort
You stop supporting Big Cheese™, soon you find yourself sleeping with the dust bunnies...
@AlexB33 Read Thomas Pynchon's new book Shadow Ticket lol
cheese causes more people to die by bedsheet entanglement because cheese makes tummy hurt and tummy hurt means restless sleep, which increases likelihood of becoming entangled in bedsheets and dying !
ice cream causes drownings because you're supposed to wait to go swimming after you eat
what if the fire station was on fire #law
This lesson HELPED a ton!!!!
#help for the ice cream and drowning weather example, i instantly just thought it was a coincedence. how is that example different from the cheese consumption example? is this just an instinctual thing we have to know of how close or how far some things can be from causing or affecting another?
i am struggling with the same question #help
I am also wondering the same
(A) per capita cheese consumption correlates with (B) the number of people who die by becoming entangled in their bedsheets
What if obesity is a (c) outside contributer to cheese consumption and being unable to untangle out of bed sheets thus leading to death?
This could happen all over the world, it could be tested, and direct evidence can be checked by looking at obesity rates. It might not be true, but isn't it a possibility?
It couldn't actually be tested because there are too many confounding variables to properly create a statistical correlation.
people in Wisconsin def get real kinky with the cheese
A causes B
B causes A
Some secret third thing causes A and B
I lied, there's nothing
I would love to know how this directly relates to questions we may see on the LSAT. I've read that you're not supposed to make any assumptions on the test, but it seems like all 4 types of hypotheses do just that?
a serial arsonist sends letters to fire departments telling them how many firefighters to send depending on the size of the fire they are going to start
@JosephWendt "The arsonist had oddly shaped feet" - Ron Burgundy
#Farenheit451 for the firefighters causing fires :)
Incorrect, it is godzilla.
Or firefighters training by setting buildings on fire to practice extinguishing them.
Not me just sitting here trying to ponder if an increase in temperature could lead to more cheese consumption, heavy fat caloric intake to keep warm, and also people being in bed under their sheets more for warmth, wondering if C could indeed cause A and B separately lol...
Are the writers ok? These examples are starting to get a little weird lol. I mean the ice cream one got dark real fast.
They've made me laugh haha
all of these examples are super common lol. they are commonly used in 1st year political science classes.
yeah, I was going to say...I've heard some of these examples elsewhere.
Remind me not to eat Ice Cream while re-reading this lesson. Hello darkness my old friend!
How do we know in hypothesis 3 that we don't have a C? Do we have to always come up with C or is it given in the stimulus?
Maybe eating cheese causes people's stomachs to churn and those people churn in bed and churn up dead.
My theory for the last scenario and general teaching suggestions
What if wealth is correlated with cheese consumption per capita, and with an elderly population, and an elderly population is correlated with bedsheet entanglement death?
If so then:
==ca==> cheese consumption per capita
Wealth of nation
==ca==> older population ==ca==> death by entanglement in bedsheets
I suppose because there is no =ca=> cheese =ca=> old =ca=> bedsheet death 'straight' link, there is no causation. There is correlation but both cheese and death are separately caused by the same thing 'wealth', but that does not mean one causes the other, just that both have the same cause, which if you go back far enough is true of everything. I think the above 'fork' presentation shows this well visually.
Suggested notations:
causation: =ca=>
correlation:
#feedback
correlation:
doesn't show for some reason (leftwards arrow, equals sign, 'co', equals sign, rightwards arrow, superficially looking like a weird biconditional indicating the tentative nature of the relationship, once causation is established, if established, the above suggested causation sign can be used).