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
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Li An, PhD
Associate Professor
Department of Geography
San Diego State University
http://www-rohan.sdsu.edu/~lian/
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