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# Statistics 2010 Notes- Sampling Basics STAT 2010

AU

GPA 3.4

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This 6 page Class Notes was uploaded by Abbey Johnson on Tuesday August 23, 2016. The Class Notes belongs to STAT 2010 at Auburn University taught by Dr. Jinyan Fan in Fall 2016. Since its upload, it has received 8 views. For similar materials see Statistics for Social and Behavioral Sciences in Statistics at Auburn University.

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Date Created: 08/23/16

Lecture 3-Sampling Basics Tuesday, August 23,2:00 PM • ○ ^ Review from the previous lecture • Population and Sample ○ Population- everybody that the research is targeting § Population results are called population parameters ○ Sample- the subset of the population that actually participates in the research § Makes data collection manageable § Sample results are called sample statistics § You can calculate the average of something based on a sample ○ In statistics in general, you want to generalize a population by taking a sample from it Lecture 3-Sampling Basics Tuesday, August 23,2:00 PM • ○ ^ Review from the previous lecture • Population and Sample ○ Population- everybody that the research is targeting § Population results are called population parameters ○ Sample- the subset of the population that actually participates in the research § Makes data collection manageable § Sample results are called sample statistics § You can calculate the average of something based on a sample ○ In statistics in general, you want to generalize a population by taking a sample from it § Makes data collection manageable § Sample results are called sample statistics § You can calculate the average of something based on a sample ○ In statistics in general, you want to generalize a population by taking a sample from it § For example, you want to study something about cats, but it's impossible to study all cats of a population, so you take a sample of these cats □ Ideally, the sample will generalize the entire population if you do it right (a reoccurring theme in statistics) □ You'll never have a perfect, exact statistic of a population just by taking a sample, but this is just accepted knowledge ○ § Population- Auburn undergraduate students § Sample- the ones eating lunch on a Tuesday at 12:30pm ○ § Population- university faculty in the United States § Sample- the 250 professors in the study • Sampling Error ○ Sampling Error- the discrepancy between the population parameter and the sample statistic § There will always be sampling errors because it is not a 100% accurate representation of the entire population, its just better to have a smaller one than a bigger one ○ It has 2 sources: § Sampling Variability(not as concerning to have this, it's always there) □ One way to reduce sampling variability is to increase the sample size § Makes data collection manageable § Sample results are called sample statistics § You can calculate the average of something based on a sample ○ In statistics in general, you want to generalize a population by taking a sample from it § For example, you want to study something about cats, but it's impossible to study all cats of a population, so you take a sample of these cats □ Ideally, the sample will generalize the entire population if you do it right (a reoccurring theme in statistics) □ You'll never have a perfect, exact statistic of a population just by taking a sample, but this is just accepted knowledge ○ § Population- Auburn undergraduate students § Sample- the ones eating lunch on a Tuesday at 12:30pm ○ § Population- university faculty in the United States § Sample- the 250 professors in the study • Sampling Error ○ Sampling Error- the discrepancy between the population parameter and the sample statistic § There will always be sampling errors because it is not a 100% accurate representation of the entire population, its just better to have a smaller one than a bigger one ○ It has 2 sources: § Sampling Variability(not as concerning to have this, it's always there) □ One way to reduce sampling variability is to increase the sample size entire population, its just better to have a smaller one than a bigger one It has 2 sources: ○ § Sampling Variability(not as concerning to have this, it's always there) □ One way to reduce sampling variability is to increase the sample size □ A large sample will have a smaller variability because it will be a closer representation of the entire population § Sampling Bias(this should not be in your sample, it's bad to have a sampling bias) □ Definition of sampling bias: the difference between your sample and the actual population results because of your method of choosing the sample □ How to avoid sampling bias? Always use a random sample ○ § entire population, its just better to have a smaller one than a bigger one It has 2 sources: ○ § Sampling Variability(not as concerning to have this, it's always there) □ One way to reduce sampling variability is to increase the sample size □ A large sample will have a smaller variability because it will be a closer representation of the entire population § Sampling Bias(this should not be in your sample, it's bad to have a sampling bias) □ Definition of sampling bias: the difference between your sample and the actual population results because of your method of choosing the sample □ How to avoid sampling bias? Always use a random sample ○ §

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