Interpretation of N-way ANOVA results using different models

I have been trying to understand the function anovan in MATLAB to perform n-way ANOVA to test the effects of multiple factors on my data. what caught my eyes when I read the help page for this function http://www.mathworks.com/help/toolbox/stats/anovan.html is that, in their example, the p value for factor X1 changes from being insignficant (p>0.05) to being significant (p<0.05) when the model is changed from default('linear') to 'interaction'. How should I interpret this result? Also, what if I don't know whether the two factors I'm testing have any interaction or not, which model should I use?

Thanks!

Answer 1

[the cyclist]

wjg,

As a general rule, a model with more parameters will provide a better fit to the data, but that does not make it a better model. You can do apply an F test that tests whether the additional terms result in a better model (which weighs the improvement in fit against the removal of a degree of freedom). You might want to read the following Wikipedia page:

http://en.wikipedia.org/wiki/F-test#Regression_problems