# BIOL 243 Lecture Notes - Lecture 2: Sampling Error, Cohort Study, Confounding

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25 Aug 2016
School
Department
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BIOL 243
Week 2
Sampling & sampling error
Ideal sampling process:
Select a group of units that are a good representation for the population
Can be broken down to 4 components
1. Units must have a known and non-zero probability of being included in your sample
2. Unbiased, should not just be one sample, must include the true population
3. Independent, When selecting one unit to be in your sample, should not influence other
units being selected
4. Each possible sample has equal chance of being selected
Sampling error (Sampling variation)
It is NOT a mistake
A natural variation from one random sample to another
Designing observational studies
Main goal is to characterize an existing population
Characterize existing populations
Find correlations
Have confounding variables
Have multiple study designs
Draw backs are correlative, but not causal
oCorrelation shows the trends, but causation is rarely definable with this method
Confounding variables, are variables we haven’t observed, but are likely driving
relations to those we have observed
Retrospective vs prospective
Retrospective Prospective
Looks back in time
More likely to find correlation
Look at all who have a disease
Look for commonalities for those that
have a disease that are not common in
those who are not diseased
Can be done for a snap shot in time
Prone to confounding errors
Studies are short
Looks forward in time
More likely to find causation
represent the population well
Look for individuals who disease as they
age
Find a relation between disease and age
Studies take longer
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Cross-sectional surveys
1. Simple random survey
2. Stratified survey
3. Cluster survey
Try to characterize something in an entire population
For correlations within the population
4. Case-control study
5. Cohort study
Look at subparts of a population and look for correlation (People who have disease vs those
that don’t
1) Simple random survey
a. Ideal sampling design for taking a survey for a snapshot in time
a.i. Example: finding peoples income
b. Avoids problems such as bias and independence
2) Stratified survey
a. Division of a population into separate groups called Strata
b. Strata is a character used to separate sampling units
c. Income differences in a city, strata would be neighbourhoods
d. Each strata is then sampled
3) Cluster survey
a. Division of population into groups called Clusters
b. Clusters are then random chosen for sampling
4) Case-control study
a. Do not characterize entire population, only subsets
b. Useful in rare events
c. Look for traits common in one group compared to another that cause that rare event
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