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Sociological Statistics
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Published on Jan 15, 2019
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MORE DECKS TO EXPLORE
PRESENTATION OUTLINE
1.
The Central Limit Theorem
Chapter 8
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allenran 917
2.
generalize from a sample to a population
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Mervyn Chan
3.
Taking probability and sampling to the next level
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Martin Adams
4.
example
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dylan nolte
5.
the real magic
quantifying likelihood we are right
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Krystal Ng
6.
Central Limit Theorem's superpowers
(a list of 4 exciting things)
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JESHOOTS.COM
7.
1) if we have info on the population, we can infer about any given sample
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Will Echols
8.
2) if we have info about a proper sample, we can infer about the population
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rawpixel
9.
3) If we have data on a population and a sample, we can determine how weird that sample is
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Aislinn Ritchie
10.
4) if we have info on two samples, we can determine whether they likely came from the same population
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Arend Vermazeren
11.
info = averages & variation
variation between groups vs. within groups
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Howard J Duncan
12.
race - does it genetically exist?
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Omar Lopez
13.
CLT: degree of confidence
it's not magic, it's probability
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Sharon McCutcheon
14.
How? Repeated samples' means for any population will be roughly normally distributed around the population's actual mean.
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JeepersMedia
15.
whoa Nelly...
A population has a mean
Multiple samples each have a mean
Most of the sample means will be near the population mean, but not all
Sample means will be normally distributed, so 68% will be within 1 SD
This is all true even if the population is not normally distributed
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Kirsten LaChance
16.
household income distribution example
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Kelly Sikkema
17.
in a representative sample
our best guess of the mean of any sample is the population mean
the proportions in the sample should roughly mirror the population
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pedrosimoes7
18.
more samples?
the means will get closer to being normal
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monkeyinfez
19.
larger sample sizes?
the tighter the distribution bunches (less affected by outliers)
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Centophobia
20.
Caveat: the Central Limit Theorem needs sample sizes of at least 30 to work
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kreg.steppe
21.
68% within 1 standard deviation
95% within 2, 99.7% within 3
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Cristian Escobar
22.
standard error:
standard deviation of the sample means
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Q9F
23.
standard deviation = dispersion in 1 group, avg distance from each case to the mean
standard error = dispersion of the sample means (multiple samples), average distance of each sample's mean from the true mean
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Aldo Schumann
24.
big standard error?
means are spread out in the samples
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publicenergy
25.
put another way, sample means will cluster around the population mean less tightly if there's lots of variation in the population
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Andrew Ridley
26.
Standard error formula
for understanding, not calculating
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rawpixel
27.
standard deviation in the numerator
sample size in the denominator
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Rakesh JV
28.
we don't often know the st deviation in the population
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Katie Tegtmeyer
29.
THE PAYOFF
68% of sample means will be within 1 st error of the true mean
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Stephanie Klepacki
30.
...as long as we have big enough samples
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Chandra Achberger
31.
put it all together
means of large samples will normally distribute around population mean, even if population isn't normal
most will be close to the pop. mean
probability says 68% within 1 st. error, 95% within 2 st. errors
if it isn't likely chance, there's probably some other factor in play
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woodleywonderworks
Leeda Copley
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