Have you ever wondered what happens after running a two-way ANOVA and finding significant interactions between factors? I’m going to share my experience with carbon flux data and the insights I gained from exploring estimated marginal means (EMMs).
When I performed a two-way ANOVA using ‘year type’ and ‘ecosystem’ as factors, I found significant interactions between them. The next step was to compute EMMs and their differences within each ecosystem. But then, I had some questions: Why were the EMMs identical to the mean of the corresponding group? And how were the confidence intervals computed?
As I dug deeper, I realized that EMMs are not just simple means, but rather a way to estimate the marginal means of a factor while accounting for the interactions with other factors. This is especially important when sample sizes vary across groups, like in my case.
The confidence intervals, on the other hand, are computed using the variance of the EMMs and the degrees of freedom. This allows us to make inferences about the population means.
My takeaway from this experience is that a significant p-value indicates a significant difference between dry years and reference years in the corresponding ecosystem. But it’s essential to go beyond the p-value and explore the EMMs to gain a deeper understanding of the interactions between factors.
I hope this helps others who may be facing similar questions after running an ANOVA. And if you have any insights to share, feel free to comment!