How to make statistical sense of this experiment: I have conducted an experiment but I am now unsure of how to say, from a statistics point of view, that the data supports or not that a certain phenomenon has occurred, meaning it could be mere measurement error.
This was the experiment: a sample of steel had its ferrite(one common constituent of steels) content measured by a  certain device 10 times, resulting in 10 values(likely hovering around the true value), a mean value  and the standard deviation(is this really what should be being computed here?).
Then the sample received a heat treatment and again had its ferrite content measured by the same device 10 times, resulting in 10 values, a mean value and the standard deviation.
Let´s assume the values for the mean and standard deviation for the untreated sample are, respectively, 25 and 1.2. And the values for the treated sample are 23 and 1. 
How can I make the proper statistical treatment here? How to go about computing how certain  one can be when ascertaining the phenomenon did/did not happen? 
EDIT: Actually, in the experiment, one sample was used to measure the non-heat treated ferrite content. Then four sets of samples, of the very same material/same batch of course, were heat treated for different lengths of time at the same temperature, each of the four sets treated at a different temperature. 
For each set of samples, all of the samples were heat treated together, then at say, 300 seconds, one sample was taken out of the oven. Then, at 600 seconds another, at 6000 seconds another, and so on.  
The ferrite content of all samples was measured using the same device.
The samples that were heat treated for long times have numbers that show clearly that something happened, even without proper statistical analysis. The problem is dealing with the samples treated for short times, as they showed numbers that are similar to the untreated sample, hence the need to test them for statistical significance.
 A: Of course, it would be better if you had repeated this experiment
for several steel specimens. But it is almost always desirable to have more
data. The question here is what you can do with the data you have.
It seems you had $n_1 = 10$ ferrite determinations before heat
treatment with $\bar X_1 = 25$ and $s_1 = 1.2.$ Then after
heat treatment, you made $n_2 = 10$ comparable determinations
with $\bar X_2 = 23$ and $s_2 = 1.0.$
Then, assuming the data are not markedly far from normal, a two-sample t test is appropriate for assessing
whether the decrease in ferrite content is statistically significant.
You can look at the formulas in Sect. 1.3.5.3 of the NIST Engineering Handbook (search two sample t NIST online). For your data, I did the computation using Minitab
statistical software, as follows:
 Two-Sample T-Test and CI 

 Sample   N   Mean  StDev  SE Mean
 1       10  25.00   1.20     0.38
 2       10  23.00   1.00     0.32

 Difference = mu (1) - mu (2)
 Estimate for difference:  2.000
 95% CI for difference:  (0.962, 3.038)
 T-Test of difference = 0 (vs not =): 
   T-Value = 4.05  P-Value = 0.001  DF = 18
Both use Pooled StDev = 1.1045

I used the 'pooled' version of the test, in which one assumes
that the population variances are equal.
The results show that the decrease in ferrite content is
statistically significant: (1) The P-value 0.001 indicates
that if there truly was no change, there would be only 
about one chance in a thousand seeing a decrease as large as 2.
(2) A 95% confidence interval for the true decrease is
$(0.962, 3.038)$, which does not include $0$ difference
as believable value. (3) Also, not shown in the Minitab printout:
a 'critical value' is 2.101; if the absolute value $|T| = 4.05 > 2.101$, that is evidence at the 5% significance level that
the two population means differ. (Any one of these three
interpretations, suffices to answer your question.)
If you had provided data for ferrite measurements at higher
temperatures or for longer heating times, one could use a
more complicated ANOVA (analysis of variance) or regression design to
decide whether any significant differences exist, and (to an
extent) also to say which heat treatments differ from which
other ones. You seem to feel confident, on this occasion, that these effects are obviously real. But bear in mind for future reference
that it is possible to deal with more than one such comparison
at a time in a statistical model.

Notes (sort of like lawyers' fine print): 
(1) One might have used the Welch (separate variances)
version of the two-sample t test, also referenced in the NIST
handbook. It would give the same value of $T,$ used degrees of
freedom $df = 17,$ and given a P-value that is not noticeably
different. Here, the CI is $(0.958, 3.042)$ also essentially
the same as above, and the critical value is 2.110.
(2) If, before seeing the data, you expected the ferrite
content to decrease after the heat treatment, you could have
used a one-sided test, resulting in an even smaller P-value.
(3) Of course, one could quibble and say that it is the
passage of time, or some other reason other than heat treatment
that caused the decrease in ferrite content (drift in accuracy of ferrite determination? humidity? lunar
phase?). But the time
order of untreated preceding treated is inevitable, and the
objection seems silly. 
(4) I see no justifiable way to
use a paired t test here because I see no natural pairing
of individual before-after measurements. Perhaps you could
run a paired experiment if it were possible to measure the
same exact pieces of steel before and after, but I don't
think that is what you did. 
(5) It would have been
preferable to see the 20 individual measurements. That would
have made it possible to do certain diagnostic tests. If you
have these individual measurements available, edit then into
your question and I will have a look. But the effect is
sufficiently large that I do not foresee any change in
interpretation.
