"The results show the Bay Area's economic inequality and its relationship with transit and urban form."
I'm not sure what they are supposed to show. It seems to show me that there's very little relationship between income and mass transit service.
I'm not sure what they are supposed to show. It seems to show me that there's very little relationship between income and mass transit service.
I'm confused as to what I'm meant to glean from this data.
Ignoring inconclusive/negative data is only problematic if it contradicts the data you choose to act on.
Talking about what didn't work is good because it stops other people from repeating failed experiments.
Finally, in this case, it does contradict the NY study.
data certainly do not have preconceived notions, but i dont think that is your point.
people are not supposed to draw conclusions from data? that sounds like exactly what people are supposed to do, if they are to survive.
is your point that humans are biased towards certain results? that does not follow from your earlier two points ("Data aren't supposed to..." and "Sure, but you're not 'supposed' to...").
why do you think humans are biased towards "positive" results?
> I'm not sure what they are supposed to show.
In other words, "in the context of what argument are you presenting this data?" Throwaway is saying that the person presenting the data needn't have an intended conclusion; it doesn't need to be part of an argument at all. It's OK, in fact preferred, for it just to be data from which you can draw conclusions, or decide there are no interesting conclusions to draw.
The primary questions of good science are what, where, when, and who. These are the questions you answer when you collect data. Once you've answered them you can address secondary questions of why and how. Asking why and how without giving priority to what, where, when, and who is putting the cart before the horse.
When you are unable to answer why and how for a given set of data, it is not meaningless. Rather, the lack of correlation or explanations just says that perhaps we need to look into this more deeply. "I've looked at the data and I don't know" is a profound statement, and it can be inspiring.
Science also has to be falsifiable, and effectively that's what these graphs do, at least as far as extrapolating from the NY study goes.
I agree it would have been more helpful if the author had presented conclusions about what the data mean or don't mean, but they aren't a priori meaningless simply because there isn't a visible correlation. No correlation, which is the rather obvious conclusion, is just as meaningful. I hope this is more clear.
you are saying that a fair conclusion is "there is no clear relationship between transit and income in the bay area", which could be the conclusions the authors were drawing (i think it's not clear exactly what conclusion if any they drew).
i actually was not thinking about it correctly in retrospect. i saw the nyc graph and i thought "manhattan = rich, everywhere else = poor". i saw these graphs and i thought "don't see anything". i assumed the conclusion from the nyc graphs was superior, because there was a clear positive relationship there. you are saying that the "don't see anything" conclusion is just as valid, even though it is not a positive relationship.
i (now) agree with you.
What do the transit vs. income graphs for SF look like?
How do the SF graphs compare to the NY ones?
Is there a clustering of rich and poor stops in SF like there is in NY?
And then finally, what are the possible explanations?
Sure, they didn't answer the last question, and you have to inspect the data to answer the second and third ones, but it's okay to provide data for other people to look at.
Surely if the first question was worth answering for NY, it's worth answering for SF. You don't answer questions simply because you expect to find something, you answer them because you're curious.
Do you know the story about Richard Feynman and the wobbling plate in the cafeteria? It's another question that "nobody wanted to answer".
Look, I'm wording myself badly today, let me try again. The question of 'is there a correlation' is worth answering but ONLY because a correlation is plausible. If you are graphing data to apply to a PLAUSIBLE hypothesis, then your work is reasonable. But if you are instead graphing random junk without any reason, you are wasting everyone's time. Data needs to cause some kind of mental connection in the viewer. That is a VERY low bar to meet. This article meets it. But not all theoretical articles do.
No conclusion is not necessarily a conclusion. Data doesn't have meaning but showing people data should have meaning.
http://www.physics.ohio-state.edu/~kilcup/262/feynman.html
But otherwise, I think we basically agree. Mostly I thought you were talking about the SF vs. NY thing and this article, not about inane investigations into arbitrary correlations (e.g., Is there a relationship between the number of steps someone takes per day and the number of spoons in their apartment? - well, actually, there probably is, especially if you start taking away knives and forks too). The most important thing I guess is to have a question that the researcher is interested in answering.