Sampling and bias

In a nutshell

Sampling is where data is collected about a group in order to make predictions about another much larger group. If a sample is biased, this means that it does not properly represent the larger group and the predictions are likely to be incorrect.

How does sampling work?

When a population is large, it is difficult to survey it entirely so samples of smaller groups are taken instead.

Definitions

Population	The large group that you want to find out information about. The group could be anything from humans to plants to animals.
Sample	Data collected about a smaller group taken from the population.
Random sample	Every member of the population has an equal chance of selection.

In order to be able to draw accurate conclusions from a sample, it must be representative of the whole population.

Characteristics of a representative sample

1.	It must be a random sample.
2.	It must be big enough: the larger the sample, the more reliable it is.

Identifying bias in a sample

Bias happens when a sample is not representative, meaning either the sample is not random or is too small.

Definitions

Biased sample

A sample that does not accurately represent the wider population.

Tip: To decide whether or not a sample is random, think about when, where and how the sample is taken and who is in it!

Example 1

Kate wants to find out information about her village. She takes a sample of 100 women in the village - this sample is not random as it excludes the men.

Example 2

Oliver wants to find out information about how many foxes there are in his garden. He counts the number he sees during the day - this sample is not random as it was only taken during the day where there is a much smaller chance of seeing a fox.

Excluding a certain group from a sample can lead to bias due to different things , such as age or gender.