need help with my peers post: In your response posts to at least two peers, discuss the following: Choose two different sampling methods from among your peers’ responses. Then, identify bias in each p
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need help with my peers post:
In your response posts to at least two peers, discuss the following:
Choose two different sampling methods from among your peers’ responses. Then, identify bias in each peer’s sample and explain how you think they could remove the bias from their sample.
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In the scenario for this discussion, if Susan were to take a sample of the male and female survey results from the largest group of students, the 12th grade, she would be using a cluster sampling. This would not be a good representation of the population because she would be disregarding the other grades. Even though the 12th grade students have the largest population, and she would be sampling both the male and female results, she is still neglecting the other students in 9th-11th grade and therefore not getting a good representation of her population.
On the other hand, if Susan used a stratified sampling, she would gain a better representation of the survey results across grades, genders and classrooms. As an example, if she were to select 10% of each grade and then evenly divide that among the number of classrooms and genders, she would get a more accurate sampling of the student body population. This would roughly be one male and one female from each classroom across all grades.
A sample is used as an indicator of the greater population. The sample is a subset of the population used to collect data and draw conclusions based on the outcome of that set in relation to the population. Samples are composed of individual measurements gathered for analysis. The sample methods that I selected above for this discussion were cluster sampling and stratified sampling. Cluster sampling is my example of what would not be a good representation for the movie selection, whereas stratified sampling is my example of what would be a good representation for the selection.
If Susan were to create a sampling of students from 9th grade alone, this would create a selection bias of undercover age. It would not accurately represent the entire population due to neglecting to include students in 10th, 11th, and 12th grades. The interests would be skewed toward a more immature crowd. Likewise, if she selected only students from 12th grade her results would be skewed toward a more mature crowd.
Susan would be better off utilizing a stratified sampling method. She could take 3 random students from each classroom to have a sample that is a solid representation of the population. The population of the school is split 50/50 by gender, so that would likely result in a fairly even split in our sample. As for grade level and classes, by selecting 3 from each class you can select a proportionate sample size to the source data.
The sample should be an accurate representation of the population, or entire collection of items, people, or things, that provides data about the population. The first sampling of students that provided data with a bias of undercover age is an example of convenience sampling. The subset of students by grade was readily available. While taking 3 random students from each classroom is an example of a stratified sampling, because first the students were divided into groups based on their classroom and then sampled randomly from within those groups.