R: t.test, am I doing this right? I have these data: 
 C <- c(6.9, 8.2, 9.4, 9.2, 7.3, 6, 8.6)
 f <- c(5.4, 7.1, 5.6, 7.6)

To find out if C and f are similar or not, I do a t.test:
t.test(C, f, var.equal=TRUE, paired=FALSE)

This returns:
    Two Sample t-test

data:  C and f
t = 2.0115, df = 9, p-value = 0.07515
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.1891484  3.2248627
sample estimates:
mean of x mean of y 
 7.942857  6.425000 

Am I correct in assuming that this means that I accept $H_0:$ no difference between C and f? - > I fail to reject H0
Upon doing a boxplot of the values, it is seen that the median seem very far apart. 
 A: Comment: Response to question in comment about boxplots.
A major factor in not being able to reject is the very small sample sizes.
Perhaps the boxplots look impressively different, but boxplots give no
visual information about sample size. I question whether the boxplot
for f with only four observations even makes sense. How do you make
an informative 'five number summary' with four observations?
Here is a 'stripchart' of the two samples (and my only excuse for Answer format
instead of another Comment). In my view it is a better graphical
description of your data, showing small sample sizes and individual observations with some overlap.
all = c(C, f);  gp = c(rep(1,7), 2,2,2,2)
stripchart(all ~ gp, ylim=c(.5,2.5), pch=19)


Seeing this stripchart, I'm a bit surprised that you could reject at the 10% level.
Note: Here are boxplots with 'notches'. Notches in the sides of the boxes represent
nonparametric confidence intervals for the medians, which are calibrated for
comparing two boxplots (in the sense that overlapping notches indicate non-significance). Here the notched boxplots are quite ugly  because the notches extend
beyond the 'hinges' (quartiles). R gives a message suggesting not to use notches here. I would never recommend this graphic for a
report, but it might be a way to illustrate that the boxplots are not impressively different.

boxplot(C, f, notch=T, col="skyblue2")
Warning message:
In bxp(list(stats = c(6, 7.1, 8.2, 8.9, 9.4, 5.4, 5.5, 6.35, 7.35,  :
  some notches went outside hinges ('box'): maybe set notch=FALSE

A: You would be incorrect in accepting H0 based on p>.05. You should use equivalence tests (TOST) for these types of conclusions (and I wouldn't use accept even if the TOST works) Here is a link to an R package that will do this for you 
http://daniellakens.blogspot.com/2016/12/tost-equivalence-testing-r-package.html
