Ecologists rely heavily upon statistics to make inferences concerning
ecological phenomena and to make management recommendations. It is
therefore important to use statistical tests that are most appropriate
for a given data-set. However, inappropriate statistical tests are
often used in the analysis of studies with categorical data (i.e.
count data or binary data). Since many types of statistical tests
have been used in artificial nests studies, a review and comparison
of these tests provides an opportunity to demonstrate the importance
of choosing the most appropriate statistical approach for conceptual
reasons as well as type I and type II errors. Artificial nests have
routinely been used to study the influences of habitat fragmentation,
and habitat edges on nest predation. I review the variety of statistical
tests used to analyze artificial nest data within the framework of
the generalized linear model and argue that logistic regression is
the most appropriate and flexible statistical test for analyzing
binary data-sets. Using artificial nest data from my own studies
and an independent data set from the medical literature as examples,
I tested equivalent data using a variety of statistical methods.
I then compared the p-values and the statistical power of these tests.
Results vary greatly among statistical methods. Methods inappropriate
for analyzing binary data often fail to yield significant results
even when differences between study groups appear large, while logistic
regression finds these differences statistically significant. Statistical
power is is 2-3 times higher for logistic regression than for other
tests. I recommend that logistic regression be used to analyze artificial
nest data and other data-sets with binary data.
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