Find a discussion of one sample t tests in your text or class notes.
And I suppose you are using some kind of software in your course.
How about looking to see what happens when these (claimed normal)
observations are used in a t test in R (or some other statistical program or using a statistical calculator).
x = c(29.4, 30.8, 30.6, 31.5, 32.1, 31.7, 30.3, 30.8)
t.test(x, mu = 32)
One Sample t-test
data: x
t = -3.6238, df = 7, p-value = 0.008468
alternative hypothesis: true mean is not equal to 32
95 percent confidence interval:
30.18222 31.61778
sample estimates:
mean of x
30.9
The P-value is given in the output. When the t statistic has DF = 7, the probability of a smaller value than 30.9 is $P(T < -3.6238) = 0.00423.$
But this is a 2-sided test (Why do I say that?), so you have to consider the probability of
a value at least as far into the upper tail. So the P-value is $0.00847$
as in the output.
pt(-3.6238, 7)
[1] 0.004233656
2 * pt(-3.6238, 7)
[1] 0.008467311
The critical value $c$ for a test at the $10\%$ level is the probability
that $|T| \ge c,$ again with DF = 7. You should be able to use use a
printed table of percentage point or quantiles of t distributions to
find the value that cut 5% from each tail of that t distribution.
Look on row DF = 7 of such a table and see if you can find something
close to $c = 1.895.$
qt(.95, 7)
[1] 1.894579
Notice that you need the actual data (or at least $\bar X$ and $S_X$
in order to find the P-value, but to find $c.$ you need only know
that you have $n = 8$ normally distributed observations.
Now match this whole answer with examples in your text or notes.