# Re: [asa] Believing Scripture but Playing by Science’s Rules

From: Iain Strachan <igd.strachan@gmail.com>
Date: Mon Feb 12 2007 - 13:15:32 EST

On 2/12/07, Freeman, Louise Margaret <lfreeman@mbc.edu> wrote:
> I don't see how stating a date in a dissertation can *not* imply endorsement
> of that date! Is the same true of his central conclusion? Can you actually
> write a 197-page scientific thesis and *not* believe what you've written to
> be true?
>

I beg to differ.

Actually, though I don't support the YEC ideas in any shape or form, I
DO, think that actually we do scientific research all the time based
on assumptions that we don't believe are true. Why is this the case?
Because the simplifying assumptions we make allow a tractable
solution.

For example, how many PhD theses (and scientific papers) are there out
there that have a linear regression trend plotted by "least squares
fitting"? How many people even know, let alone believe the underlying
assumption behind fitting a trend line (something we do almost without
thinking because Excel has a button to do it for you)? Here's the
assumption: you assume in making the fit that the data are best
modelled by the equation y = mx + c + r, where r is a residual that is
due to random processes (and errors in measurement). In choosing m
and c to minimise the sum of squares of the r variables, you are
assuming that the probability distribution or r is a Gaussian (aka
Normal distribution) whose standard deviation is independent of x.
Under this assumption, the least squares fit maximises the likelihood
of the data, and m and c are the "best fit". If the noise isn't
Gaussian, then m and c aren't the best fit and any predictions will
have a systematic bias.

Now, I'm guessing that many people who use linear regression don't
know this esoteric fact, so can't be accused of not believing it. But
to take another example: Hidden Markov Models are the subject of
many, many PhD theses out there (including mine). They are used
extensively in automated speech recognition. The model captures the
data as a sequence of discrete "states", in speech recognition each
state being a "phoneme" or component of speech. The underlying
assumption of the Hidden Markov Model is that the length of time spent
in each state (the time to pronounce each phoneme) is given by an
exponential distribution. No-one who uses Hidden Markov Models
actually BELIEVES this to be true. The true distribution is more
likely to be Gaussian. So why do we use a model that is based on
assumptions that we KNOW are false? Because (a) it works in practice
and (b) we can do the maths. If we had to model the states as
Gaussians, the resultant model would take many orders of magnitude
longer to process the data, and we wouldn't be able to do speech
recognition.

So actually I see nothing wrong or dishonest about doing science based
on assumptions that we don't believe are true - we always state "given
the following assumptions:", and we are not beholden in scientific
papers to state whether we personally believe those assumptions to be
true.

Iain

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Received on Mon Feb 12 13:15:42 2007

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