# Sampling Lesson Objectives Key Words

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Sampling Lesson Objectives Key Words
To understand different methods of sampling and sample size To identify which sampling method to use to solve a problem Key Words population sample random stratified bias

Think back to last lesson
Think back to last lesson. What problems did you have sorting through the data?

We can’t possibly use all the data.
We need to cut down the data we use. But we need to do this fairly. In analysing statistics we usually use a sample of the data.

Census Census research uses data from every single member of the population. If we wanted to do a census study of Emmanuel students’ eating habits, we would have to ask EVERY child in the school.

Costs a lot of time and money. Very difficult to get data for absolutely everybody.

Random Sampling A random sample uses data taken from some of the population chosen randomly – every member of the population has the same chance of being chosen. If we wanted to take a random sample of Emmanuel students’, we could put all the names in a list and then pick every tenth child to take data from.

Random Sample Example:
For this random sample I put all the names in alphabetical order and then chose every second child, either all the whites or all the yellows. Example: Kaysey Abbott Daniel Hazledine Ruth Merryweather Oliver Boot Sarah Hopkins Hayley Rayner Jack Buckley Holly Hulland Hannah Rodger Terri-Lee Christopher Isaac Jessica Ryder Allistair Burton-Casey Ben Lane Kieran Tetley Ebony Chambers William Leonard Rhian Thomas Joshua Coates Colette Leverton Rebecca Voce Durner Nicholas Lewin Laura Want Shannen Fretwell Ross Liggins Iain Watt Joel George Marriott Andrew Winter James Greenhalgh Wakas Masood

Some important parts of the population might be missed out.

Stratified Sample A stratified sample divides the population into categories and then a random sample can be taken from each category. The number from each category represents the proportion of the population in that category. If we wanted to take a stratified sample of Emmanuel students’, we could group the children into male and female and year groups and then take a sample from each group.

Stratified Sample Example: 1001 - 1004 1005 - 1008
For this stratified sample I’m going to group the students based on birth year and sex first. We choose at random from each group based on the group’s size. Example: Male Female Chris Isaac, Will Leonard, Andrew Winter, Joel George, James Greenhalgh, Daniel Hazeldine, Kieran Tetley, Oliver Boot Total of 8 people we use 4 Terri-Lee Buckley, Sarah Hopkins, Jessica Ryder, Colette Leverton, Kaysey Abbot, Hayley Rayner, Shannen Fretwell, Ruth Merryweather Jack Buckley, Alistair Burton-Casey, Josh Coates, Oliver Durner, Ben Lane, Nick Lewin, Ross Liggins, Jack Marriott, Wakas Masood, Iain Watt Total of 10 people we use 5 Ebony Chambers, Holly Hulland, Hannah Rodger, Rhian Thomas, Rebecca Voce, Laura Want Total of 6 people we use 3

Ensures that members of all parts of the population are considered. More time-consuming and expensive than a random sample.

Sample size Before we take a sample of the population, we must consider how large the sample should be. Large samples are more likely to be accurate and representative. But, larger samples take longer to sort and may be more expensive to collect.

Activities Read through the excellent examples on page 33 and 34.
Try questions 1, 4 and 5.

“People in Europe live longer than people in Africa.”
Homework For the first part of your Statistics coursework you will need to test the hypothesis: “People in Europe live longer than people in Africa.” Think about how we could use what we’ve learnt about sampling to help this investigation – answer the questions on the worksheet.