KISS A occurs and B occurs = correlation. A does not occur and B does occur = A is not necessary for B to occur. B will not occur if A does not occur = necessary causation (e.g., HIV and AIDS). B will occur if A occurs or if C occurs = sufficient causation (e.g., Drought or darkness will cause plant stress).
Wildlife ecology Christopher R. Ayers, AWB® Wildlife, Fisheries, and Aquaculture Mississippi State University [email protected] Office: (662) 325-8611 Cell: (804) 239-2137 -----Original Message----- From: Ecological Society of America: grants, jobs, news [mailto:[email protected]] On Behalf Of Miles Medina Sent: Wednesday, October 10, 2012 8:28 PM To: [email protected] Subject: Re: [ECOLOG-L] correlation v. causation This reminds me of a story I heard from an old Alan Watts lecture: Imagine you're looking through a small hole in a fence. There is a cat on the other side, but for the sake of the story, pretend you've never seen a cat before. And the entire cat is not visible all at once through this small hole. So, as it walks by from the time to time, you first see the head, then the trunk, then the tail. From this perspective, it seems reasonable to say that the head causes the tail. After all, whenever you see the tail, it's always preceded by the head. Obviously, though, the cat is just a cat, and talking about which part causes the other part is pretty silly. So, the point is, our own perspective plays into the question. Also, I would add, in response to a comment above.. someone said correlation implies causation. Yes it may, of course, but let's not forget that there could be a third variable that causes the two correlated ones originally in question. Rushing to a conclusion about some simple relationship might completely ignore the deeper dynamic. MM On Wed, Oct 10, 2012 at 12:11 PM, Resetarits, William < [email protected]> wrote: > Seems relevant at this time to remind ourselves of the statistical > meaning of correlation vs its popular use and perhaps more importantly > why Ecology and Evolutionary Biology became and continue to be > experimental sciences whenever possible. > > >From the classic stats text Steele and Torrie (1980 p 277). > > "Correlation measures a co-relation, a joint property of two variables. > Where variables are jointly affected because of external influences, > correlation may offer the most logical approach to that analysis of > the data. Regression deals primarily with the means of one variable > and how their location changes with another variable. Š. Correlation > is associated with descriptive techniques: regression has to do with a > relation between population means and the values of a concomitant > variable. Thus, whereas a correlation coefficient tells us something > abut a joint relationship between variables, a regression coefficient > tells us that if we alter the value of the independent variable then > we can expect the dependent variable to alter by a certain amount on > the average, sampling variation making it unlikely that precisely the > stated amount of change will be observed." > > Thus, in Tom's example the correlation between churches and drunks > implies not that either drives variation in the other, but simply that > they covary, which may be a result of simple coincidence or that the > are both responding to a common external driver. So, when most lay > people talk about correlation, especially in looking for causal > drivers, they are really implying regression and have a priori chosen > one variable as the putative independent variable. Both approaches > may IMPLY causation, regression by one of a pair of variables and > correlation by some external driver affecting both variables, but neither can > establish causation. > > Only well-designed experiments actually establish causation. These > may identify causal factors phenomenologically (without necessarily > identifying mechanism) or mechanistically, but either way are the only > method for definitively establishing causal relationships. When used > as the ultimate analysis (rather than for hypothesis generation) The > elaborate and increasing sophisticated statistical methods of > regression and elaborate models are quite simply a substitute for > situations where experiments are infeasible. Good to never lose sight of > that. > > > William J. Resetarits, Jr > Professor > Department of Biological Sciences > Texas Tech University > Lubbock, Texas 79409-3131 > Phone: (806) 742-2710, ext.300 > Fax (806) 742-2963 > > > > > On 10/9/12 8:01 PM, "Thomas J. Givnish" <[email protected]> wrote: > > >The number of drunks per city is very strongly correlated with the > >number of churches per city. > > > >On 10/09/12, Lee Dyer wrote: > >> My favorite *introduction* to this vast topic can be found in the > >>first few chapters of Bill Shipley's short book, Cause and > >>Correlation in Biology (2000). A quote from his book: > >> "In fact, with few exceptions, correlation does imply causation. > >>If we observe a systematic relationship between two variables, and > >>we have ruled out the likelihood that this is simply due to a random > >>coincidence, then something must be causing this relationship." > >> > >> ******************************************************* > >> Lee Dyer > >> Biology Dept. 0314 > >> UNR 1664 N Virginia St > >> Reno, NV 89557 > >> > >> > >> > >> OR > >> > >> > >> > >> 585 Robin St > >> Reno, NV 89509 > >> > >> > >> > >> Email: [email protected] > >> Web: www.caterpillars.org > >> phone: 504-220-9391 (cell) > >> 775-784-1360 (office) > >> > >> > >> > >> > >> > Date: Tue, 9 Oct 2012 10:57:34 -0500 > >> > From: [email protected] > >> > Subject: Re: [ECOLOG-L] correlation v. causation > >> > To: [email protected] > >> > > >> > Hi Shelley, others, > >> > > >> > Slate recently had a great article on correlation and causation > >> > with a historical perspective. > >> > > >> > My favorite line: "'No, correlation does not imply causation, but > >> > it sure as hell provides a hint." > >> > > >> > > >> > http://www.slate.com/articles/health_and_science/science/2012/10/corre > lat > >>ion_does_not_imply_causation_how_the_internet_fell_in_love_with_a_st > >>ats_c > >>lass_clich_.html > >> > > >> > > >> > > >> > > >> > > >> > > >> > > >> > "Having nothing better to do, I set fire to the prairie." > >> > -- Francis Chadron, 1839, Fort Clark, North Dakota > >> > > >> > http://www.devanmcgranahan.info > > > >-- > > Thomas J. Givnish > > Henry Allan Gleason Professor of Botany University of Wisconsin > > > > [email protected] > > http://botany.wisc.edu/givnish/Givnish/Welcome.html >
