[lit-ideas] Re: Three arguments against quantitative social "science" as science
- From: palma <palmaadriano@xxxxxxxxx>
- To: "lit-ideas@xxxxxxxxxxxxx" <lit-ideas@xxxxxxxxxxxxx>
- Date: Tue, 2 Sep 2014 15:58:40 +0200
consider.
the plants died beCAUSE the gardeners did not water them
your claim is that there is no causal link, hence the correlations are a
set of non finite cardinality.
hence the plants died since philosophers say stupid things.
what is the notion of proof at stake?
note that according to this (which is not Hume's by the bye) conception,
people do not starve because they do not eat, since their death correlates
equally well with the passing of time.
regards
On Tue, Sep 2, 2014 at 11:49 AM, Torgeir Fjeld <torgeir_fjeld@xxxxxxxx>
wrote:
> Are the social sciences scientific? Do they deal with causality in ways
> that are sufficiently similar to that of the natural sciences to lay claim
> to the recognition accrued by biology, physics, chemistry, etc? Here's
> three arguments as to why quantitative methods in social "sciences" do not
> vouch for these disciplines`s status as "sciences".
>
> (1) Causality can't be proven -- ala Hume. It's possible to demonstrate
> that the the white billiard ball moved first, the black ball second, and
> that there was an encounter beteen the two. But there is no necessary
> relation between these statements and to say that the encounter was the
> _cause_ of the black ball's movement.
>
> (2) Correlation is not causality. Statistics can demonstrate correlation
> between to variables, say education and longevity. This is not to say,
> though, that a causal relation has been established. The relation between
> these variables are purely convetional -- some variables are considered to
> be more "fundamental" than others. Such variables include income,
> gender?sex, ethnicity etc. However, it is possible to concieve of a
> relation whereby education has some relation to gender -- say, access to
> and knowledge of sex change operations.
>
> (3) Hempel's law of symmetry ("nomology") between explanation and
> prediction doesn't apply. Say that I throw a dice 60 times. The result if
> the experiment is that the number 1 came up 10 times, the number 2 came up
> 10 times, etc. Now, from these results there is no way for me to estimate
> the probability of the result of the next throw of the dice: It is still
> 1:6 for each side of the dice. And, importantly, even if I had thriown the
> dice 60 times with the same side coming up, the chances for that sime side
> coming up the 61st time would _still be_ 1:6. The kinds of macro statistics
> assembled by the socalled quantitative social sciences are of the same
> kind: They purportedly show how large data sets behave, but their pridctive
> force are negligeble.
>
>
> Med vennlig hilsen / Yours sincerely,
>
> Torgeir Fjeld
>
> http://independent.academia.edu/TorgeirFjeld
>
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