If this is an spss question then I don't know the answer. I can't see anything wrong with the data.
But it seems to be statistically significant.
> t.test(d$QuizGrade ~ d$Section)
Welch Two Sample t-test
data: d$QuizGrade by d$Section
t = -2.2401, df =...
Well, you asked a question and he answered exactly that question. He can be somewhat robot-like sometimes. But if you ask a more specific question about what you have difficulties with, he might answer that.
You are correct that "there can also be 0 breakdowns". But please not that the table give the cumulative distribution function.
Here are both of them:
1 0.04076220 0.0407622
2 0.13043905 0.1712013
3 0.20870248 0.3799037
4 0.22261598 0.6025197
5 0.17809279 0.7806125...
But it seems we need to give an interval (like M[4:5,2:3]) to enter a matrix.
M[3,1] <- -1.2
m22 <- matrix(1:4, 2,2)
M[4:5,2:3] <- m22
Try Poisson regression instead with "plant medium" as explanatory factor. It is similar to anova but uses the Poisson distribution (for number of shoots) instead of the normal distribution. (It uses the dependent variable "shoots" given the explanatory factor, thus it corresponds to the residuals.)
They are not. We just notice things.
But don't you think that it is rude to ask for an explanation when someone else is also busy doing the same thing? Would it been OK for you if nobody had noticed?
But you can answer me: Why should I or anybody else help you? Give me a motivation!
Since you are
"counting seedlings emerging from soil samples".
Then it seems more appropriate to replace the normal distribution assumption (that is implicit in traditional anova) with a distribution that is based on counts, e.g. the Poisson distribution (start with that) or the negative...
NO! Of course not. The t-test is still based on the normal distribution. The distribution must still be somewhat similar to the normal distribution. But the t-test is relatively robust to moderate deviations from normality. With the Welch test it is robust to non constant variance. But the Mann...
It happens quite often that when two explanatory variables, x1 and x2, are correlated, that each one of them are statistically significant in their influence on the dependent variable y (cancer/no cancer), but that when both of them are included, then none of them are formally significant...