On Sat, 2010-02-06 at 21:44 -0800, Li An wrote:
> Dear Ecologers,
> 
> In testing ecological models, we often use t-test as a way to compare 
> our model results with observed data. If they are close enough, we 
> obtain more confidence about our model. However, in most traditional 
> situations, we put "no difference" as the null and regarded it as the 
> default. This means that unless we find substantial evidence, we would 
> retain the null hypothesis. For instance, we can use this type of test 
> to examine if a drug has a noticeable effect.
> 
> In our model performance situation (testing observed data = predicted 
> numbers from a model, assuming data independence), I argue that we 
> should keep the alternative hypothesis as the default, making every 
> effort to find substantial evidence to support the null hypothesis (if 
> unable, we retain the alternative hypothesis related to inequality 
> between the model predictions and the data). In this case, we can still 
> use the traditional test statistic such as z or p values, but interpret 
> the results differently. Rather than using the criterion of p > 0.05 (or 
> Z<1.96 or t < a big number) to retain the null hypothesis, we should use 
> a more strict standard--e.g., p > a much larger number (e.g., 0.9) or z 
> < a much smaller number (e.g.,0.125), to retain the null hypothesis 
> about equality between the model predictions and the data. This seems 
> mofrea philosophical issue. Does this make sense?
> 
> Li

You  might like to look at the field of equivalency testing. Some
references cited in the 'equivalence' package by Andrew Robinson for R
are:

Robinson, A.P., and R.E. Froese. 2004. Model validation using
equivalence tests. Ecological Modelling 176, 349–358.

Wellek, S. 2003. Testing statistical hypotheses of equivalence. Chapman
and Hall/CRC. 284 pp.

Westlake, W.J. 1981. Response to T.B.L. Kirkwood: bioequivalence testing
- a need to rethink. Biometrics 37, 589-594.

HTH

G
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