Suppose we have a single sample. The questions we might want to answer are these:
In order to be reasonably confident that our inferences are correct, we need to establish some facts about the distribution of the data:
Non-normality, outliers and serial correlation can all invalidate inferences made by standard parametric tests like Student's t test. It is much better in cases with non-normality and/or outliers to use a non-parametric technique such as Wilcoxon's signed-rank test. If there is serial correlation in the data, then you need to use time series analysis or mixed-effects models.
To see what is involved in summarizing a single sample, read the data called y from the file called das.txt:
names(data)  "y" attach(data)
As usual, we begin with a set of single sample plots: an index plot (scatterplot with a single argument, in which data are plotted in the order in which they appear in the dataframe), a box-and-whisker plot (see p. 155) and a frequency plot (a histogram with bin-widths chosen by R):
par(mfrow=c(2,2)) plot(y) boxplot(y) hist(y,main="") ...