This is generally good (specifically, the description of confounding is accurate in a way that overview courses often aren't), but misses a few fine points. (1) correlation in the data that is due to chance is not, in fact, a correlation relationship between the phenomena in the real world. So "Chance" is not a valid alternative explanation for a correlation. It's an alternative explanation for an apparent (illusory) correlation. (2) For spurious correlations (real correlations that are not due to a causal relationship), there is much more to it than reverse causality and confounding. Confounding is often thought of as a catch-all, but is a very specific type of spurious correlation. There are others. A crucial one is collider bias, where conditioning on a common downstream effect produces a spurious correlation.
If you have live, I highly recommend the LSAT Course: Causal Reasoning (Foundational). They go over this and give it an acronym CREA. It's really helpful
Keeping in mind these 4 hypotheses are definitely going to be helpful. I often confuse between 1 and 2 when I assume that A causes B and not the other way around when the phenomenon is common sense. I have to be attentive to what the sentence says and think of these 4 possibilities. It could be a 3rd factor that causes both A and B or there isn't even a causal relationship at all. Guess I gotta train and familiarize :)
Opposite of A. I would say something like smoke seems like the remedy or solution to lung cancer.
H2
H3
People want to fit in. People want to be seen as cool. People see media that depicts the bad boy archetype and want to emulate it to be seen in a better light. People also see videos that show the affects and still choose to take a chance with smoking. People end up choosing to smoke might end up with lung cancer but that is not the only way to get lung cancer. Having Lung cancer does not guarantee that you were smoking.
I wonder how I might implement this to explain a correlation that I don't have any knowledge on. The answer here is simple to find because of my knowledge. How will I know which is right on the exam when they use more obscure facts or events.
Will there be context? Or will it always be a simpler example.
I am confused so do we have to choose one "correct" Hypothesis? For Example - Hypothesis 2: B causes A wouldn't be accurate --> Lung Cancer causes people to smoke. Common Sense.. #help
Another possibly helpful example that I remember on correlation vs. causation is sales of ice cream and drownings, which have a high correlation. However, obviously there is no causation, as ice cream doesn't cause drownings. Instead, phenomenon C, or hot days, caused an increase in ice cream sales (A), and an increase in swimming, resulting in more drownings, or (B).
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29 comments
Correlation: = two things move together (not saying one causes the other)
Causation: = one thing makes the other happen
Test: If you can say “X happened → made Y happen,” that’s causation. If you can only say “X and Y show up together,” that’s correlation.
Reminder Trick:
This is generally good (specifically, the description of confounding is accurate in a way that overview courses often aren't), but misses a few fine points. (1) correlation in the data that is due to chance is not, in fact, a correlation relationship between the phenomena in the real world. So "Chance" is not a valid alternative explanation for a correlation. It's an alternative explanation for an apparent (illusory) correlation. (2) For spurious correlations (real correlations that are not due to a causal relationship), there is much more to it than reverse causality and confounding. Confounding is often thought of as a catch-all, but is a very specific type of spurious correlation. There are others. A crucial one is collider bias, where conditioning on a common downstream effect produces a spurious correlation.
"c" in this video, in statistics, is called the confounding variable: A variable that influences both a and b independent of each other.
So when I encounter correlation, the common hypotheses should be running through my head before reading AC's?
do you ever watch a lesson video and then realize you were zoned out the whole time and then have to watch it over again.....
If you have live, I highly recommend the LSAT Course: Causal Reasoning (Foundational). They go over this and give it an acronym CREA. It's really helpful
Keeping in mind these 4 hypotheses are definitely going to be helpful. I often confuse between 1 and 2 when I assume that A causes B and not the other way around when the phenomenon is common sense. I have to be attentive to what the sentence says and think of these 4 possibilities. It could be a 3rd factor that causes both A and B or there isn't even a causal relationship at all. Guess I gotta train and familiarize :)
Correlation: Researchers have found that cavities increase with the more glucose they add to rats teeth
1: Glucose causes cavities
2: cavities causes glucose presence in teeth
3: diet caused the high amounts of cavities and glucose
4: when the study was done on humans, there was no increase in cavities. No causation
H1
Lung cancer causes people to smoke.
Opposite of A. I would say something like smoke seems like the remedy or solution to lung cancer.
H2
H3
People want to fit in. People want to be seen as cool. People see media that depicts the bad boy archetype and want to emulate it to be seen in a better light. People also see videos that show the affects and still choose to take a chance with smoking. People end up choosing to smoke might end up with lung cancer but that is not the only way to get lung cancer. Having Lung cancer does not guarantee that you were smoking.
H4
What's the expression for hypothesis 4 in lawgic? If no (A →B) is that /A → /B? Is there a A
→B (strikethrough of →) ?lol I'm-a-well-paid-tobacco-industry-lawyer suit :0
what the value of law industry !
I wonder how I might implement this to explain a correlation that I don't have any knowledge on. The answer here is simple to find because of my knowledge. How will I know which is right on the exam when they use more obscure facts or events.
Will there be context? Or will it always be a simpler example.
My "I'm-a-well-paid-tobacco-industry-lawyer suit," tells me Philip did nothing wrong. Coincidences sure are interesting.
https://en.wikipedia.org/wiki/Philip_Morris_USA
I am confused so do we have to choose one "correct" Hypothesis? For Example - Hypothesis 2: B causes A wouldn't be accurate --> Lung Cancer causes people to smoke. Common Sense.. #help
Another possibly helpful example that I remember on correlation vs. causation is sales of ice cream and drownings, which have a high correlation. However, obviously there is no causation, as ice cream doesn't cause drownings. Instead, phenomenon C, or hot days, caused an increase in ice cream sales (A), and an increase in swimming, resulting in more drownings, or (B).