Why the Cloud Cannot Obscure the Scientific Method 137
aproposofwhat noted Ars Technica's rebuttal to
yesterday's story about "The End of Theory: The Data Deluge Makes the Scientific Method Obsolete." The response is titled "Why the cloud cannot obscure the Scientific Method," and is a good follow up to the discussion.
Don't blame the author's incompetence (Score:2, Interesting)
I'm moonlighting in bioinformatics (Score:5, Interesting)
And can back up this rebuttal with a practical example. I am a physicist, I know sod all about blood samples, or proteins, or cancer. I get a pile of mass spec data (about a billion data points or so on some days) and through binning, background subtraction, and a string of other statistical witchcraft I produce a set of peaks labeled according to intensity and significance.
This does not make me a cancer researcher. This data has to go back to the cancer guys and they have to pick out the Biomarkers and thus develop new diagnostic tests, based on principles that I don't understand. I am master of the information but entirely blind as far as the science is concerned. Same goes for google.
Rise of Engineering over Science? (Score:5, Interesting)
I have always viewed this debate in the context of scientist vs. engineer. That is one who views data as "good and true" vs. "good enough". That's not a slam on engineers (I am one), but a reflection of the balance between the two. A scientist that never applies theory sits in an empty room. An engineer who build things with out science, sits in a cluttered room surrounded by useless objects.
I do find interesting though that the advent of "google data" may indicate a flip in order of the two disciplines. Historically (IMHO) science has led engineering. A theoretical breakthrough, provable by the scientific method, may take years to give birth to a practical application. Now, with enormous piles of data and the knowledge that "good enough" is often good enough, we may be creating useful objects that will take science many years to explain and model.
The biggest issue and omission in both of these pieces is that this "cloud" of data does not represent "truth" (as the scientist may seek), but rather a summation or averaging of the "perception of truth" as seen by the individual authors. The cloud, therefore, is only as useful as human's ability to divine truth without the scientific method.
My two cents. :)
Re:Correlation is not causation (Score:2, Interesting)
Fine. I'll try to restate my point using more specific language.
The fact that correlation does not imply causation isn't nearly as troublesome as the volume of "Remember folks correlation!=causation" would have us believe; lacking other evidence, it is a reasonable assumption to start with.
Re:I agree, but... (Score:5, Interesting)
Yes, I think that prediction without explanation is fascinating, but I don't know if it's what I like about science :) Have you ever heard Lenard Smith speak? I saw him at SAMSI, but his MSRI talk is online and is roughly the same. He's a statistician who works in exactly this.
Some fancy-pants technique he has is better at predicting the future behavior of chaotic systems (like van der Pol circuits or the weather) than physical models. But he also points out that these predictions don't tell you what type of data to collect to make better predictions, and that they don't generalize. One nice "model" he has can predict the weather at Heathrow better than physical weather models (from the same inputs: wind speed, temperature, pressure, etc), but it's useless for predicting the weather in Kinshasa until the model is re-trained.
I think these types of data analysis tools will be very important in the future, but they won't replace the explanatory power of models. Just like how scientific computing is useful, but never replaced actual experiments.
Re:Correlation is not causation (Score:3, Interesting)
Re:Correlation is not causation (Score:4, Interesting)
The large scale genetic association studies are a great example. There was a day that you could publish a paper solely describing a correlation between a variant in gene X and its association with disease Y. However, because of the way we do statistics in science, sooner or later you'll find a statistically significant correlation simply due to chance alone. In fact the epidemiologist John ioannidis wrote an article [plosjournals.org] about this (that I believe appeared on Slashdot as well). Now you're often required to show some kind of experimental validation that there is a biological basis that verifies the statistical correlation. The scientific method is not going away anytime soon.
Number one pet peeve with my doctor (Score:2, Interesting)
To the vast majority of practicing physicians I've met "cause" just doesn't seem to be the important question. Which I think is why things happen like my pharmacist declaring that two drugs prescribed by my doctor are going to cancel each others effects or why I take a drug to treat a painful toenail and end up with bleeding in my stomach.
Re:Correlation is not causation (Score:2, Interesting)
Mathematics is the language of science, and there has never been an advancement in either one without an accompanying advance in the other.
A mathematician might "gush" about clouds of data, and work on the mathematics of it, but if he insisted it made science obsolete he'd be tossed out on his ear.
Oh, and string theory? That was the physicists. The mathematicians were pissed off that someone found a use for topology, which we considered pure mathematics for its own sake and unconnected to the real world. Damned physicists ruined our fun.
Re:Number one pet peeve with my doctor (Score:3, Interesting)
You are unfortunately quite correct and it's very frustrating. I speak as a physician with a strong background in experimental biology. MOST medical research is complete and utter garbage. Statistically correct garbage, but crap none the less. However, in defense of my current field - it's awfully hard to do "experiments" in human research. Hell, it was hard enough to do on eurkaryotic culture cells. Which is why much of the underpinning on modern biological sciences was done on "simple" organisms like bacteria and phages.
Another, more empiric way of looking at what most of what medical science is doing comes from the realization that if you "cure" or "improve" a disease process, at some levels it makes no difference whether you understood what you're doing or just managed to get a valid correlation between treatment and effect. To use a previous example, when you taken a statin to reduce cholesterol, you (as the patient) don't do this to "lower your cholesterol" - you do it so you live longer / healthier / disease free. The statin -> reduce cholesterol correlation may have led researchers to the treatment regimen in the first place, but the end point is staying alive longer. Thus, if the actual mechanism for that is channeling his noodliness, the treatment still works.
Of course, that's not science (or at least not very good science). But it IS the state of medical therapy.
Biology is fiendishly complex and we, as usual, make lots of baby steps and stutters. However, anybody that thinks a doctor in the latter part of this century is going to look like back at 2010 medical practice and decide it's "butchery [fast-rewind.com]" is smoking some good stuff.
Links need thought (Score:3, Interesting)