## 2.11 Final example

As a demonstration of sampling schemes
(Marshman and Dunn Submitted),
consider taking a *non-random* sample of
10% of the pixels of an image
(Fig. 2.3).
What is the image?
Seeing the **big picture** is hard using these non-random samples.

In contrast,
taking *simple random sample* makes the **big picture** much clearer
(Fig. 2.4).

Indeed,
*any* type of random sample makes seeing the **big picture** easier.

For example,
for a *cluster sample* we treat each *column* as a cluster,
and select some *columns* at random.
Then,
the entire chosen columns are selected.

For a *systematic sample*,
we take:

- every 20th pixel for a 5% sample;
- every 10th pixel for a 10% sample;
- every 4th pixel for a 25% sample; and
- every second pixel for a 50% sample.

For a *multi-stage sample* we select some columns at random,
then select some pixels in those columns at random.

For a *stratified sample*,
we select :

- a simple random sample from the background greenery, and then
- a simple random sample from the person.

These two are then combined to get an overall random sample.