I realize similar questions have been asked several times, but I think this is different enough to warrant a seperate question.
I have poisson distributed catch data with independent variables of Tool (the thing used to catch the fish), Species, and Habitat. I am most interested in testing whether different tools catch fish at different rates (the tool*species interaction). All of these predictors are fixed, discrete factors. I know that none of these factors are the best predictor for catch (that would be a bunch of environmental variables that I don't have and am not particularly interested in). In order to test this, I ran a Poisson distributed GLM with the above listed Model Effects.
Given this situation, is it appropriate to interpret the significance (or lack thereof ) of the factor effects even when the model is highly non-signifant? If not, is there another way than a GLM that I can test whether the factor levels have any significant impact on the response variable?