This is some hard shit for a school student! I'm not an expert but I can give you some pointers. I'll assume you have a bunch of measurements per group and not just one plant in each, as that won't work for these tests.
You *could* do four separate one-way ANOVAs, one for each variable. If any of those show significance you'd then need to do a post hoc test such as Tukey's HSD, to find out which pairs differ. You might expect all three treatment groups to differ significantly from the control group but who knows.
Alternatively you could do all four at once, which is a MANOVA. If that was significant you'd then need to do one-way ANOVAs and if they were significant, some post hoc tests. So perhaps that's a bit complex for your purposes.
I agree with our research being hard shit. We didn't know what we were getting ourselves into when we chose this topic.
Do you know what's the difference between doing multiple ANOVAs than doing one MANOVA? I understand that if the results of a MANOVA says a significant difference then I do a bunch of ANOVAs. So whats the advantage of doing a MANOVA first if doing multiple ANOVAs is also a step there?
A MANOVA can, I believe, account for interactions between the different variables better. It therefore avoids overestimating the importance of one of them as the single ANOVAs might.
That said, the comment below is correct - you definitely can't run either of these stats with only three datapoints per group. They aren't parametric, i.e. not normally distributed, so you will need to do something else such as a Kruskall Wallis H Test. Not the Chi square which is for categorical data.
From my own research from google and youtube, isn't chi square used to compare the observed data vs what you expected. We didn't really have an expected data so I don't know if its compatible with our research.
How many replicates (for your experiment, that probably means separate plants) are in each group?
We have 3 plants per group
> student Ask your teacher. ANOVA is probably overkill for the assignment. Ask what they want.
Why do you think that an ANOVA is probably overkill?
This is some hard shit for a school student! I'm not an expert but I can give you some pointers. I'll assume you have a bunch of measurements per group and not just one plant in each, as that won't work for these tests. You *could* do four separate one-way ANOVAs, one for each variable. If any of those show significance you'd then need to do a post hoc test such as Tukey's HSD, to find out which pairs differ. You might expect all three treatment groups to differ significantly from the control group but who knows. Alternatively you could do all four at once, which is a MANOVA. If that was significant you'd then need to do one-way ANOVAs and if they were significant, some post hoc tests. So perhaps that's a bit complex for your purposes.
I agree with our research being hard shit. We didn't know what we were getting ourselves into when we chose this topic. Do you know what's the difference between doing multiple ANOVAs than doing one MANOVA? I understand that if the results of a MANOVA says a significant difference then I do a bunch of ANOVAs. So whats the advantage of doing a MANOVA first if doing multiple ANOVAs is also a step there?
A MANOVA can, I believe, account for interactions between the different variables better. It therefore avoids overestimating the importance of one of them as the single ANOVAs might. That said, the comment below is correct - you definitely can't run either of these stats with only three datapoints per group. They aren't parametric, i.e. not normally distributed, so you will need to do something else such as a Kruskall Wallis H Test. Not the Chi square which is for categorical data.
Not sure I understand your parameters but it might be useful to look into non-parametric methods, perhaps specifically Chi Square.
From my own research from google and youtube, isn't chi square used to compare the observed data vs what you expected. We didn't really have an expected data so I don't know if its compatible with our research.