So, is the moral of this lesson to say that if we could run a perfect experiment to back up our casual statements then they would be stronger? That seemed pretty self-explanatory.
@BenBeecham I see this lesson as more of a tale of what a perfect casual relationship should have to be assumed to be true, and if any of these factors are missing, look for those as the flaw in the experiment.
What if because they were forced to smoke a pack of cigarettes a day they had less time or desire to exercise, and this lack of exercise caused the cancer?
I've seen weaken questions and strengthen questions where it talks about populations and experiments and if it would strengthen or weaken the arguement
Hi! I don't know if this helps much, but there are lots of questions on the exam (at least that I've seen) that ask about the populations being sampled in a study and whether the population is big enough and/or representative enough to actually be able to draw a valid conclusion for causation. It might be that understanding how sampling is performed (to avoid errors) helps in understanding how a study presented in questions on the exam might have errors in sampling/experimentation that leads their results to be incorrect (which I assume might be helpful for Weaken questions). Hope this helped!
im confuse dhow we are supposed to apply this knowledge to the test. like yes obviously running a test like this would reveal results that can help us with the link, but we aren't going to be doing that on the test, or seeing these numbers spewed at us on the test. someone let me know
Knowing the characteristics of the impossibly ideal experiment (like the one described above) allows you in WSE questions to WSE (pardon my redundancies) the experiment in the stimulus. To weaken it would to take away or move further from ideal by noting that the sample selected was not randomized. To strengthen it would be to move it closer to the ideal experiment by adding a control group, for example. To evaluate the experiment would be to ask what the ideal experiment would be asking to uncover the causal connection between two things. While this knowledge is not tested directly, it's super important to keep in mind when you encounter a stimulus that describes an experiment to see what it is doing right/wrong.
It was appropriate to use 140k instead of 150k because the assumption with this experiment is that both of the control groups are equally split up. Meaning if there are 10k people in control group A (smoking group) living on street X then there will also be 10k people in control group B (non-smoking group) living on street X. Thus, if you have the equal distribution between control groups then you have eliminated all other potential causes of lung cancer. Since 10k people got cancer that did not smoke, then we know that there was an alternate cause for 10k people in the smoking control group as well. (Remember that all other things are equal so you can make this assumption). Therefore, out of the 150k people who got lung cancer in group A, 140k (150k- 10k from other reasons) can be attributed to smoking.
Note that this is all hypothetical because it is more complicated in real life
The whole purpose of knowing this information is that the LSAT uses the scientific method (experimental method) in order to frame causal arguments. In some instances, you'll be told that some control group was compared to something else and the researcher is arguing that it must be true one caused the other or whatever they are asserting. By knowing the basics of an ideal (valid) experiment, we can quickly evaluate if the researcher in question did follow the scientific method correctly and if they did not, then we can conclude the argument is weak, that it must be false, etc. It's just another way of evaluating a causal argument when numbers and percentages are used to argue causation rather than subconsciously going through the 4 possible hypotheses to evaluate the relationship in question.
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36 comments
So, is the moral of this lesson to say that if we could run a perfect experiment to back up our casual statements then they would be stronger? That seemed pretty self-explanatory.
@BenBeecham I see this lesson as more of a tale of what a perfect casual relationship should have to be assumed to be true, and if any of these factors are missing, look for those as the flaw in the experiment.
what if the non-smoking group got lung cancer because of second-hand smoking. thus they got cancer from smoking anyway, kind of, no? haha
any psych majors here?? this has been the easiest part of the lessons for me lol
@ReeseWalter bio major and can relate lmao
gonna hop on the bandwagon and also suggest this be a video :(
This really needed to be a video. What is going on here
How are RCTs directly applicable to answer LSAT qs, or is this supposed to be more of a general lesson to understand the bigger pic?
#feedback- THIS SHOULD BE A VIDEO! COME ON
What if because they were forced to smoke a pack of cigarettes a day they had less time or desire to exercise, and this lack of exercise caused the cancer?
Totally ethical.
Don't let Mr. Beast see this
@generallypreparedforthings I GAVE 2500 PEOPLE CANCER (FOR SCIENCE!) (CRAZY)
If my cigarettes are paid for, SIGN ME UP
"phenomena" is plural
#feedback this should be a video
Realistically, how does this apply to the exam?
Also, it would be very beneficial to be able to highlight and edit the text itself. Please consider adding this feature.
I've seen weaken questions and strengthen questions where it talks about populations and experiments and if it would strengthen or weaken the arguement
Hi! I don't know if this helps much, but there are lots of questions on the exam (at least that I've seen) that ask about the populations being sampled in a study and whether the population is big enough and/or representative enough to actually be able to draw a valid conclusion for causation. It might be that understanding how sampling is performed (to avoid errors) helps in understanding how a study presented in questions on the exam might have errors in sampling/experimentation that leads their results to be incorrect (which I assume might be helpful for Weaken questions). Hope this helped!
@emiliasadowicz929 I like videos.
@emiliasadowicz929 #feedback
im confuse dhow we are supposed to apply this knowledge to the test. like yes obviously running a test like this would reveal results that can help us with the link, but we aren't going to be doing that on the test, or seeing these numbers spewed at us on the test. someone let me know
Knowing the characteristics of the impossibly ideal experiment (like the one described above) allows you in WSE questions to WSE (pardon my redundancies) the experiment in the stimulus. To weaken it would to take away or move further from ideal by noting that the sample selected was not randomized. To strengthen it would be to move it closer to the ideal experiment by adding a control group, for example. To evaluate the experiment would be to ask what the ideal experiment would be asking to uncover the causal connection between two things. While this knowledge is not tested directly, it's super important to keep in mind when you encounter a stimulus that describes an experiment to see what it is doing right/wrong.
I believe it's just to see the bigger picture.
soooo....the scientific method basically?
#feedback it would be awesome if we could highlight certain phrases/passages in a lesson!
that one statistics course i had to take for my political science major, thank youuuuu
As a statistics major, this is just common sense!
Having a science background is finally beneficial for me
Can anyone reexplain why it was appropriate to use 140k vs 150k?
It was appropriate to use 140k instead of 150k because the assumption with this experiment is that both of the control groups are equally split up. Meaning if there are 10k people in control group A (smoking group) living on street X then there will also be 10k people in control group B (non-smoking group) living on street X. Thus, if you have the equal distribution between control groups then you have eliminated all other potential causes of lung cancer. Since 10k people got cancer that did not smoke, then we know that there was an alternate cause for 10k people in the smoking control group as well. (Remember that all other things are equal so you can make this assumption). Therefore, out of the 150k people who got lung cancer in group A, 140k (150k- 10k from other reasons) can be attributed to smoking.
Note that this is all hypothetical because it is more complicated in real life
Thank you for explaining it like this.
How do we run an experiment on something we have no additional information about? #help
I'm in the exact same boat. How in the world are we supposed to attain results of an experiment without it being entirely arbitrary?
The whole purpose of knowing this information is that the LSAT uses the scientific method (experimental method) in order to frame causal arguments. In some instances, you'll be told that some control group was compared to something else and the researcher is arguing that it must be true one caused the other or whatever they are asserting. By knowing the basics of an ideal (valid) experiment, we can quickly evaluate if the researcher in question did follow the scientific method correctly and if they did not, then we can conclude the argument is weak, that it must be false, etc. It's just another way of evaluating a causal argument when numbers and percentages are used to argue causation rather than subconsciously going through the 4 possible hypotheses to evaluate the relationship in question.
Thank you for this! I was like why am I reading this and why is this relevant?