STATISTICS Course
Descriptive statistics
You need to come up with a model for your distribution. Use plots, functions, etc., and try to find as many properties as you can. This is the easiest part of the statistics (at least for nonmathematicians).
You will use normal plots, histograms, and box plots a lot. The first thing to do when having data in your hand would be to check the variables you are interested in with a histogram or a box plot. If you notice outliers with the boxplot (see next part), deal with it. You may notice something, and by crosschecking other diagrams, you may come up with ideas.
 look at the moments of your distribution
 check if your distribution is following a normal distribution (useful in tests)
 try to generate another distribution and compare it with your sample
 check if your variables are independent/dependant
 ...
Outliers
An outlier (valeurs anormales/aberrantes/extrêmes
) is a value that looks out of place when observing your distribution. They are impacting your work a lot because they are impacting the mean.
Let's say you got some grades like 10,10,12
and your teacher made a mistake and submitted 10,10,120
.

mean(c(10,10,120)) = 46.66/20
(impacted a lot) 
median(c(10,10,120)) = 10/20
(not really impacted)
You can notice outliers easily with a boxplot, and you should remove/ignore/fix them. The median is not affected by outliers, so it's more robust.
QQplot
The QuantileQuantile plot (QQplot) is a plot in which we are comparing the quantiles of our distribution with the quantile of another one. It is used to check if it's likely that our data is following a distribution.
qqnorm(ech$var)
# qqnorm(ech$var, datax = TRUE)
# test with poisson distribution
qqline(rpois(100, mean(ech$var)))
We can see on this graph that it does not seem, that our sample is following a Poisson distribution.
Side note
This is a hint, try to remember distribution properties because that might help.
 we know that for a Poisson distribution, mean=variance=parameter
 if we are observing a $mean=3$, $var \simeq 3$, then we could check a Poisson distribution with $\lambda=3$
 this is not an accurate hint, just a hint as to how you could try to think (if you are lost, that is).