GGR270H1 Lecture Notes - Statistical Inference, Systematic Sampling, Transect

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30 Nov 2011
October 19, 2011
Sampling and Estimation
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
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
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