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NZAMT NZQA NZ Grapher NZ Maths Census at School Study It Khan Academy Desmos

Different Sampling Methods

Inference HOME | Achievement Objectives | Overview | Statistical Cycle
- Write an introduction | Using NZgrapher | Discussing sample statistics
- Box plots | Discussing the boxplot & dotplot | Sampling methods | Sample variability and size
- Informal confidence interval | Comparing two populations - discussion
- Writing a report | Revision

7

Sampling methods

What different sampling methods are possible?
Sampling | sampling methods (great videos)

What are the advantages and disadvantages of each method?
Types of sampling link to explorable.com
Choosing a sampling method (link to changing minds.com)
Sampling methods (link to gs.org)

Remember the aim: The sample must be representative of the population

Once we have a sample the data should be checked for errors (cleaned data)

Description of sampling method and justification of sampling method choice (practice)

Class notes, Blank notes

Sampling Video link

 

 

 

For AS 2.9 you need to know about different sampling methods
But only need to do a random sample.

Discussion of sampling methods (for statistical insight rather than execution)

 

Description

Advantages

Disadvantages

  • simple random
  • cluster
  • systematic
  • stratified
   

 

 

In the assessment do a simple random sample (discuss how)
And discuss if any other sampling method may be more appropriate and why?

 

Reasons for sampling:

Include time and cost considerations, lack of access to the entire population and the nature of the data collection or test, eg: A blood test verses the breaking strain of fishing line.

Features of a good sample,
One that represents the population and the sample size is sufficiently large.

There is no statistical requirement that a sample be a proportion of the population; a well designed sampling process is more likely to produce a representative sample than a large sample poorly selected sample.

It is randomly chosen: each member of the population has the same chance of being included in the sample.

Cleaning the data checks for data errors (eg 3.4m or 340cm)

 

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