> <>;_ p!bjbj,E,E 4(N/N/pppr$#######$')X$$,$###h#跘i#@#B$0r$#** #*#$$$r$*p : Issues in Level 2 NCEA AS 91264 (2.9) Inference
This document was prepared by Mike Camden, Nicola Petty, Anne Lawrence, Anna-Marie Martin, Jeremy Brocklehurst and Robyn Headifen on behalf of the NZ Statistical Association's Education Committee, for NZQA and for publication on the CensusAtSchool site, in March and April 2015. The purpose is to support the continual improvement of learning and assessment in statistics.
We welcome further discussion. Please contact m.camden@clear.net.nz or other Committee members.
Recommendations for resources
Provide support for teachers including professional development and resources including tasks and exemplars of student work that illustrate contexts where the students do not have access to the population data, and that require or show students description of their sampling method.
Remove from NZQA and Census at School websites examples of tasks in which sampling does not make sense eg where it would be feasible to use data from the whole population.
Recommended changes to the standard
We recommend a change to the clarification document for AS 91264, as soon as feasible, that provides a path for teachers through these issues.
We also suggest a number of changes to the wording of the standard under explanatory note 3. In particular we are not sure about the intent behind the bullet 'discuss sample distribution'. We suggest a rethink and rewording for this bullet point. Other specific suggestions include rewording the first bullet point, inserting another bullet point under this and rewording the current second bullet point so a student need only take one sample so that explanatory note 3 would then start:
Using the statistical enquiry cycle to make an inference involves:
posing an appropriate investigative comparison question about a population
devising an appropriate method of selecting a sample
selecting a random sample
Issues in the teaching and assessing of sampling
Sampling is an essential concept which students should be learning as part of their study of statistics. There are a number of key understandings to be developed including the reason for taking a sample, the importance of a sample being representative and the nature of sampling variability.
The reason for taking a sample
We take a sample when it is not possible to analyse data from the whole population. This may be because it is too expensive, time consuming or destructive to study every object or person in the population.
The importance of a sample being representativeIf the sample is biased, or in some way not representative of the target population, we cannot make a robust inference about the population. Although random sampling methods are commonly used, they do not guarantee that the sample generated will be representative (such as, a random sample could include lots of extreme values, just by chance).
The nature of sampling variabilityUnderstanding how samples may differ from each other and from the parent population is important for understanding ideas around sampling distributions.
As sampling is an essential part of the curriculum, it is important that sampling be assessed in a meaningful way, beyond something which can be included as a sentence learned by rote and put into context for an assessment.
Sampling is assessed in achievement standard AS 91264 (2.9) Inference which states in explanatory note 3:
Using the statistical enquiry cycle to make an inference involves:
posing an appropriate investigative comparison question from a given set of population data
selecting random samples
selecting and using appropriate displays and measures
discussing sample distributions
discussing sampling variability, including the variability of estimates
making an inference
communicating findings in a conclusion.
The clarification document for this achievement standard (Dec 2014) states: The investigation involves selecting random samples from the population groups and using information from the samples to make an inference about the population groups. When we do not have access to statistical analysis software, it is reasonable to take a sample from a population database if it is not possible to do the analysis of the whole population by hand, even though the data is available.
However, statistical software such as iNZight, NZGrapher or even Excel, enables analysis of large datasets. The conditions of assessment state: Students are expected to have access to appropriate technology. For statistics standards this would include statistical software. We would argue that technology should be used in the assessment of this standard. Under these circumstances, it is artificial to take a sample. In this situation, sampling from a given data set is not meaningful - if you had access to the entire data set, you would analyse that rather than take a sample. You don't need to (should not) make inferences from population data. This does make it difficult for students to develop a good understanding of the reason for sampling.
In order to help students make sense of the sampling they are asked to do in this assessment, the situation (context) should be one where you would not expect to have access to the population data.
In the real world, if you want to sample (randomly) then you need a frame of some sort, for the population. In the artificial world, the frame has all the data. When teachers and students sample from a given population, they should be clear about what they're doing. They're using a few demographic variables from the dataset as their frame, and ignoring the rest. When they have the sample, they retrieve the other variables for the sampled units. In an ideal world, we have a frame of the population, containing some demographic variable. We sample from this, then go and extract lots more data, previously not existing, from the sampled items. To make this more real to the students, we suggest that the teacher keeps the population data hidden. Teachers could use a virtual environment such as CaS and the Island (created by Michael Bulmer) to do something like this, with the advantage that the other data really is hidden from the students. The students devise their sampling method, and then that is used to generate a random sample from the (hidden) population.
With the emphasis on student understanding of the key principles of generating a random sample, students description of their sampling method should be a key part of the assessment.
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