1. Sampling

2. Why sample?
Sometimes you have a large amount of data and you do not want to analyse all of it. In a case like this you can analyse a sample of the data, a small proportion of it.
How do you decide which data to analyse? Can you still rely of the results to represent the whole population of data?

3. Sampling methods
We are going to look at three different sampling methodologies
Systematic Sampling
Random Sampling
Stratified Sampling

4. Systematic Sampling
Members of the sample are chosen by picking a random starting point and then selecting data at a fixed interval.
e.g. you might pick every 5th piece of data or
Start with data piece number 3 and then pick every 10th piece of data.

5. Random Sampling
Effectively, you put all the data in a hat, decide how many pieces of data you want to use and draw out this many pieces of data. Every piece of data has an equal chance of being picked.

6. Stratified Sampling
The sample data is selected to be in the same proportions as a key characteristic of the population.
e.g. if 40% of the population is over 18 and 60% is under 18 you will select your sample to have the same characteristics. If your sample is to be 10 pieces of data 40% or 4 will be for people over 18 and 60% or 6 will be selected from those under 18.

7. An example
Here is some data for the number of units of alcohol that 30 people drank last week. The red data is for women and the blue for men. 10 5 3 12 16 8 7 11 2 13 9 4 15 14 1

8. Sampling methods I want to choose 5 pieces of data for my sample:-
Systematic – I want 5 pieces of data from divided by 5 gives me 6; so I will pick every 6th piece of data.
Random – I will pull 5 pieces of data out of an imaginary hat.
Stratified – 12 pieces of data (40%) is from men and 60% from women. To match that, my 5 pieces of data will be 2 from men and 3 from women.

9. My data This is the data I selected:- 8 12 10 13 7 11 5 9 14 4 3
Systematic
Random
Stratified 8 12 10 13 7 11 5 9 14 4 3
Average
8.6
8.8
7.8