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ggr270 oct 19 notes

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Damian Dupuy

October 19, 2011 Sampling and Estimation Sampling aim of inferential statistics is to generalize about characteristics of larger population so need a process to obtain a sample sampling can be spatial or non-spatial where things are located will either matter or they won't there, an essential skill for any geographers to have Why sample? necessary in cases of extremely large populations efficient and cost-effective way of understanding the population highly detailed information can be obtained easily allows for follow-up activity or repetition asking same question years down the road longitudinal surveys - sample them again in the future Sampling Error what influences our choice of type is sampling error if a sample is representative, then it will accurately reflect the characteristics of the population, without bias if it is a good representative of the population, then we can say any data or any info or any conclusions from that sample, we can apply it to the population with minimal error as possible bias influences all your results element of randomness must be introduced to preserve the representative sample can never eliminate bias, only minimize it. Reducing bias means reducing error precision and accuracy help categorize sources of error larger the sample size, the more precise it will be, the more precise they are, the more representative it is of the population can have precise results, but can be not accurate Sampling Designs Number of different sampling designs exist: simple random systematic sampling ex. choosing every 5th person in the classroom ensure everybody is representative an
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