One of my personal favorites:
Chronobiologia. 1991 Jan-Mar;18(1):1-8.
Sunspots and hip fractures.
Caniggia M, Scala C.
Abstract
In this paper a remarkable statistical link between sunspot cycles and
prevalence of hip fractures in the elderly is shown. Hip fractures in old
people are due to: 1. increased bone fragility for metabolic bone disease;
2. increased propension to fall. Though it is obvious that a correlation
does not imply any causal relationship, reasonable conjectures can be
allowed. The hypothesis of an 11-year cyclic variation of ultraviolet
radiation as a cause of hip fractures is untenable; one may better assume
that solar flares can negatively influence the nervous postural regulation
leading to a greater propensity to accidental falls.
PMID: 1935412 [PubMed - indexed for MEDLINE]
Note the implication ("reasonable conjectures can be allowed") that the
correlation should *mean* something...
-----------------------------------------------------
> Date: Wed, 10 Oct 2012 21:27:39 -0400
> From: Miles Medina <[email protected]>
> Subject: Re: 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
> > meanin=
> g
> > of correlation vs its popular use and perhaps more importantly why
> > Ecolog=
> y
> > 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. =C5=A0. 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
> > abu=
> t
> > 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
> > implie=
> s
> > 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
> > >numbe=
> r
> > >of churches per city.
> > >
> > >On 10/09/12, Lee Dyer wrote:
> > >> My favorite *introduction* to this vast topic can be found in the
> > >> firs=
> t
> > >>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/correlat
> > >>ion_does_not_imply_causation_how_the_internet_fell_in_love_with_a_stats=
> _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
--
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Dr. John A. Berges
Associate Professor
Dept. Biological Sciences
& School of Freshwater Sciences
U. Wisconsin-Milwaukee
Milwaukee, WI 53211 USA
http://pantherfile.uwm.edu/berges/www/
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
