Sampling Techniques

Sampling Techniques

The most important thing when taking a sample is that it is representative of the population. In other words, we want to make sure there is no bias that may affect our results. There are different ways to collect a sample. We'll go through some of them now.

Random Sampling

An example of random sampling is numbers being drawn out in the lottery. Every number has an equal probability of being chosen.Each individual is chosen at random (by chance). In other words, each individual has the same probability of being chosen.

Stratified Sampling

Think of a pack of jelly beans. There are lots of different colours in the pack aren't there? Instead of considering them as a whole group of jellybeans, we could divide them up by colour into subgroups. 

Stratification is the process of dividing a group into subgroups with the same characteristics before we draw our random sample. Then we look at the size of each subgroup as a fraction of the total population. The number of items from each subgroup that are included in the sample should be in the same ratio as the amount they represent of the total population.

For example, say we decide to survey $50$50 students to find out what types of music the students at our high school liked best. It is likely that Year $7$7 students may have a different taste in music to Year $12$12 students. 

Here is a list of how many students are in each year and how we would calculate the number of students from each year we would need to survey to create a stratified sample:

School Year	Number of Students	Proportional Number for Sample
$7$7	$200$200	$\frac{200}{1000}\times50=10$2001000​×50=10
$8$8	$180$180	$\frac{180}{1000}\times50=9$1801000​×50=9
$9$9	$200$200	$\frac{200}{1000}\times50=10$2001000​×50=10
$10$10	$140$140	$\frac{140}{1000}\times50=7$1401000​×50=7
$11$11	$100$100	$\frac{100}{1000}\times50=5$1001000​×50=5
$12$12	$180$180	$\frac{180}{1000}\times50=9$1801000​×50=9
Total	$1000$1000	$50$50

 

 

Systematic Sampling

If we use systematic sampling, we are basically picking one in every $n$n^th item. From the sample, a starting point is chosen at random, and items are chosen at regular intervals. For example, we may choose every fifth name from a list or call every tenth business in the phone book.

 

Examples

Question 1

Question 2

Question 3

 

 

Even though we may not get it perfectly right, we always aim to make our sample to be representative of the wider population as possible. This way we can draw conclusions and make sensible decisions for that population. Take some time now to investigate sampling techniques further.