survey and experiments flaws in LSAT stimulus? Forum

[cavalier2015]

what kind of things should I be on the lookout for when I read surveys and experiments in the LSAT LR sections?

[hillz]

One thing is percentages v. numbers, i.e. 30% of women like the color pink while only 10% of men like the color pink doesn't necessarily mean that more women like pink than men. More men than women could have been surveyed, so maybe 10% of men = 100 men while 30% of women = 30 women.

[dontdoitkid]

Always take note of the goal of the survey/experiment, what population type would make most sense for an objective application of that survey/experiment, and what the stimulus actually included. For example, you might get something along the lines of "A recent of University of Miami Students revealed that a large majority of those surveyed opposed the new town noise curfew. Therefore, it is obvious that the Miami municipal election results were incorrect, since a majority of Miami citizens are opposed to the curfew"

It's kind of an obvious made up example, but you'll see a lot of the "sample unrepresentative of the general population" questions. I view those almost as power ups on a timed PT because if you know what to look for than you can answer it correctly very quickly and add time to deal with more difficult questions.

Also, be on the look out for comparing percentages of different populations. You might get something along the lines of "80% of town A is in support of Candidate Johnson, while only 50% of town B is in support of candidate Sullivan. Therefor, candidate Johnson will win the election." An issue with this would be that town A could be a 10 person bum town with one broken stoplight, whereas town B is New York City. Big difference there.

[Jon McCarty]

I would add to unrepresentative sample (most common) and percentage and probability the following:

-People may not have been telling the truth in a survey
While we would know that people responded a certain way, we do not know whether their responses were truthful

-The questions in the survey may have been biased
Leading questions could skew the survey

-Correlation to Causation
Just because a study found that two things happen a lot together doesn't necessarily mean that one is causing the other.

-The failure to prove a claim is not necessarily proof for the denial of that claim
This one usually states that a study was flawed and then concludes the exact opposite of what the study originally concluded. Just because the study no longer provides proof for its conclusion doesn't mean that it provides proof for the opposite of that conclusion.

Hope this helps!