Each sample represents a population of all of the trees the investigator could possibly plant, grow, and measure. Each data set gives us a sample mean that estimates the true mean of the population. We want to estimate the probability that the true means are different for the two populations that these data represent.

Two pieces of information enter in to the determination. One is the difference
between the two sample means. The bigger the difference, the more confident
we are that the true means are different. The second is the extent of error
in the two data sets combined. The greater the error, that is, the more the
data are scattered, the *less* confident we are that the true means
are different.

Unfortunately, we have to throw in just a bit more terminology at this point.
You should recall that a *hypothesis* is a testable statement. What
we call a *null hypothesis* is the prediction that there will be no
effect, no change, or no difference. What we call an *alternative hypothesis* is
the prediction that something *will* happen. There can be just one null
hypothesis, but there can be more than one alternative hypothesis.

What is the null hypothesis for this study?

What is/are the alternative hypothesis(es)?