<p>Let me suggest <a href="http://projecteuclid.org/DPubS/Repository/1.0/Disseminate?view=body&amp;id=pdf_1&amp;handle=euclid.ss/1009213726" rel="nofollow">Statistical Modeling: The Two Cultures</a> by Leo Breiman. </p>

<blockquote>
  <p><em>Abstract</em></p>
  
  <p>There are two cultures in the use of
  statistical modeling to reach
  conclusions from data. One assumes
  that the data are generated by a given
  stochastic data model. The other uses
  algorithmic models and treats the data
  mechanism as unknown. The statistical
  community has been committed to the
  almost exclusive use of data models.
  This commitment has led to irrelevant
  theory, questionable conclusions, and
  has kept statisticians from working on
  a large range of interesting current
  problems. Algorithmic modeling, both
  in theory and practice, has developed
  rapidly in fields outside statistics.
  It can be used both on large complex
  data sets and as a more accurate and
  informative alternative to data
  modeling on smaller data sets. If our
  goal as a field is to use data to
  solve problems, then we need to move
  away from exclusive dependence on data
  models and adopt a more diverse set of
  tools.</p>
</blockquote>
