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1307Re: AI-GEOSTATS: Detecting spatial autocorrelation in highly non normal data

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  • Volker Bahn
    Nov 20 7:42 AM
      The problem is not unique to testing significance of spatial correlation.
      Any traditional hypothesis test is non-sensical as you describe because we
      ALWAYS know that the null hypothesis is wrong. The question of interest is
      how wrong it is and whether the detected effect is of practical consequence.
      However, a hypothesis test does not test practical relevance of an effect
      but statistical power to detect it, which depends on sample size, inherent
      variability and also on effect size. Given a large enough sample size, one
      will always reject the null hypothesis. Why then do people still hold on to
      hypothesis tests? Because it gives them a false sense of objectivity. No one
      wants to admit that the judgement of whether an effect is of practical
      consequence is a to a certain degree inherently subjective decision (as is
      the level of alpha etc).

      Cheers

      Volker


      ----- Original Message -----
      From: "Edzer J. Pebesma" <e.pebesma@...>
      To: <trevor.middel@...>
      Cc: <ai-geostats@...>
      Sent: Thursday, November 20, 2003 5:11
      Subject: Re: AI-GEOSTATS: Detecting spatial autocorrelation in highly non
      normal data


      | Trevor,
      |
      | I always wonder what the value of testing significance of spatial
      | correlation is, and never advise to do it. See, if data are spatial, it
      | is extremely unlikely that they are spatially uncorrelated. Rejecting
      | the test is usually only a matter of collecting sufficient evidence,
      | and not at all an interesting finding, because the data were spatial.
      |
      | Probably a more real problem is: to what extent does the spatial
      | correlation present (which may be very weak!) mess up an analysis
      | that assumes independence of observations. If you choose for
      | an analysis method that addresses spatial correlation, you're always
      | on safe ground.
      |
      | If your data were collected using some form of random sampling,
      | analysis based on independent observations is perfectly valid for
      | estimating areal mean values. This does not imply that data are
      | spatially uncorrelated, but just that they may be treated independent
      | because of the sampling scheme.
      | --
      | Edzer
      |
      | trevor.middel@... wrote:
      |
      | >Hi Folks,
      | >
      | >I'm hoping someone can help steer me in the right direction.
      | >
      | >I have several sets of data acquired from acoustic surveys conducted on a
      | >small lake trout lake. The data consist of sampling units aligned in
      | >transects. Each sampling unit is 50 m in length. A mean lake trout
      density
      | >is associated with each sampling unit.
      | >
      | >I'm interested in examining whether any significant spatial
      autocorrelation
      | >in the exists in the observed distribution of lake trout. The data are
      | >highly non-normal with 75-90% of the observations being zero. Log and Ln
      | >transformations do not normalize the data.
      | >
      | >I've been doing some reading and it seems that most methods of
      quantifying
      | >spatial autocorrelation require some kind of normality in the data. Any
      | >suggestions on how I might proceed with these data?
      | >
      | >Thanks in advance for all suggestions.
      | >
      | >Apologies for the simplicity of the question, but I'm just beginning my
      | >foray into spatial statistics.
      | >
      | >Trevor Middel
      | >
      | >--
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      |
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