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Lecture 3

PSYC 202 Lecture 3: STATS 202 (week 3)
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7 Pages
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Department
Psychology
Course Code
PSYC 202
Professor
Ronald R Holden

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September 27th 2017
WEEK 3
Sampling error is not making mistakes, but rather the variation that comes with taking a
sample
Key Ideas:
- Statistical inference among populations:
o Defining a population
o Selecting a sample from the population
o Taking measurements on the individual units
o Carry out statistical analysis, including descriptive statistics and inferential
statistics that we use to make some statement about the population itself or to
compare among different populations
Sampling
- The ai of a idea saplig proess is to selet a group of uits that’s a good
representation for the statistical population
- Ideal sampling process can be broken down into 4 components:
o Units have known and non-zero probability of being included in your sample
o Unbiased
Bias is when your sample ha some systematic difference from the true
statistical population that your trying to find out about
Some of your units are less likely to be compared then some of your
others
o Independent
Selecting one sampling unit to be in your sample should not influence if
other sampling units should be included as well
o Each possible sample has equal chance of being selected
Making sure the individuals are mixed properly and representatively and
that each possible sample has an equal chance of being selected
DEFINIIONS:
1. Selection unit: every unit in the statistical population must have a chance of being in
your sample
2. Bias: selection of units cannot inadvertently favour one outcome over another on
average
3. Independence: selection of one unit cannot influence the probability that another unit
is selected
4. Equal chance for all samples: every combination of units in must be possible in your
sample
A volunteer based sample is different form a simple random sample because:
- Some people may have no chance of being included
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- Selection of people may be biased
- samples might not be independent
Sampling error
- not a mistake
- it is the variation form one random sample to another
- depending on the people in your subset your answer will be a little different and that is
sampling error
- important for being able to do statistical inference
- variation that comes about form sampling variation is the very tool that we us to make
stateets aout the thigs e hae’t see et to ake a iference about a larger
population
Observational studies
- the main goal of observational studies is to characterize something about a population
that already exists
- the major drawback to observational studies is that when you look at the characteristics
of a populatio relatioships that eerge or orrelatie ut the are’t ausal
o ou a see ho thigs tred ith eah other ut ost ofte ou a’t sa
anything about causation
- ofoudig ariales are ariales that ou hae’t osered ut are likel driving the
relationship between the variables that you have observed
- re these studies retrospective or prospective
o retrospective means they are looking back in time
e) take all the people ho hae a ertai tpe of disease ad ask hat’s
oo aogst the that is’t oo aog other people ho
do’t hae the disease
advantage to these studies is that they can be done in a snap shot of time
disadvantage is that they are really prone to confounding errors can
have correlations not cause
o prospective means they are looking forward in time
start with an initial group of people who you think is a good
representation of the population
as that group (cohort) ages you start to look at which ones start to get
diseases and ask if there is a relationship between their disease and some
of their features
eause ou ko hat’s happeed oer tie ou hae a idea of hat
comes first and that helps you with causation
not better then every study but much better then retrospective
the disadvantage is that this can take a long time
o for many big issues people are using a combination of retrospective and
prospective studies
Designs for observational studies
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find more resources at oneclass.com

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Description
September 27 2017 WEEK 3 Sampling error is not making mistakes, but rather the variation that comes with taking a sample Key Ideas: Statistical inference among populations: o Defining a population o Selecting a sample from the population o Taking measurements on the individual units o Carry out statistical analysis, including descriptive statistics and inferential statistics that we use to make some statement about the population itself or to compare among different populations Sampling The aim of an idea sampling process is to select a group of units thats a good representation for the statistical population Ideal sampling process can be broken down into 4 components: o Units have known and nonzero probability of being included in your sample o Unbiased Bias is when your sample ha some systematic difference from the true statistical population that your trying to find out about Some of your units are less likely to be compared then some of your others o Independent Selecting one sampling unit to be in your sample should not influence if other sampling units should be included as well o Each possible sample has equal chance of being selected Making sure the individuals are mixed properly and representatively and that each possible sample has an equal chance of being selected DEFINIIONS: 1. Selection unit: every unit in the statistical population must have a chance of being in your sample 2. Bias: selection of units cannot inadvertently favour one outcome over another on average 3. Independence: selection of one unit cannot influence the probability that another unit is selected 4. Equal chance for all samples: every combination of units in must be possible in your sample A volunteer based sample is different form a simple random sample because: Some people may have no chance of being included
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