Midterm 2 Study Guide

Midterm 2 Study Guide - Psychology
60,
Spring
2010
...

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Unformatted text preview: Psychology
60,
Spring
2010
 Midterm
2
Study
Guide
 
 This
study
guide
is
not
intended
to
provide
you
with
a
comprehensive
list
of
the
topics
you
need
to
 know;
rather,
this
guide
is
meant
to
guide
you
in
your
studying.
Do
not
assume
that
you
can
study
 only
these
topics
and/or
read
only
these
sections
in
the
book.
This
guide
will
not
replace
a
thorough
 reading
of
the
book
and
review
of
your
lecture
notes.

The
following
is
simply
a
list
of
topics
and
 formulas
with
which
you
should
be
most
familiar.
 
 ALSO:
Statistics
is
inherently
cumulative.
That
is,
you
need
to
know
information
from
before
the
 first
midterm
in
order
to
succeed
on
the
second
midterm
(like
how
to
calculate
a
mean,
for
 example).
I
won’t
test
you
heavily
or
specifically
on
material
from
before
the
first
midterm,
but
 understand
that
you
still
need
to
know
this
material.
Accordingly,
I
have
left
those
concepts
on
this
 study
guide.

 • The
differences
between
populations
and
samples
 • 
The
difference
between
inferential
and
descriptive
statistics
 • 
The
differences
between
statistics
and
parameters
 • 
Sampling
Error
 • 
Summation
Notation
 • 
Scales
of
measurement,
specifically
the
differences
between
the
different
scales
 • 
Characteristics
of
experimental
research
 o Manipulation
 o Control
 o Random
Assignment
 • 
Differences
between
experimental
studies
and
correlational
studies
 • 
Constructs
and
Operational
Definitions
 • 
Interpreting
frequency
graphs/histograms
 • 
Types
of
data
appropriate
for
line
graphs,
bar
graphs
and
scatter
plots
 • 
Describing
the
shape
of
distributions
using
symmetry
and
skew
 • 
Differences
between
the
different
measures
of
central
tendency
 • 
Computation
of
the
standard
deviation
for
both
populations
and
samples
 o Know
how
to
use
the
formulas
 o Know
when
to
use
each
of
the
formulas
 • 
Z‐scores
 o Computation
of
a
z‐score
for
an
individual
score
in
a
population
 o Understand
the
meaning
of
a
z‐score
 o Be
able
to
tell
which
of
two
z‐scores
is
more
extreme
 o Understand
what
extreme
means
in
the
statistical
sense
 o Converting
from
raw
scores
to
z‐score
 o Converting
from
z‐scores
to
raw
scores
 o Be
able
to
find
the
proportion
of
a
normal
distribution
between
two
z‐scores
 o Be
able
to
find
the
proportion
of
a
normal
distribution
beyond
a
z‐score
 o Be
able
to
find
the
z‐score
corresponding
to
where
a
certain
proportion
of
a
 distribution
is
beyond
that
score
 • Know
the
basic
definition
of
probability
 
 Material
Presented
Since
First
Midterm:

 
 • Distribution
of
sample
means
 o Understand
what
the
distribution
of
sample
means
represents
 o Be
able
to
locate
the
z‐score
for
a
particular
sample
mean
in
the
distribution
of
 sample
means
 o Be
able
to
calculate
the
standard
deviation
for
the
distribution
of
sample
means
for
 populations,
and
from
sample
data
 o Understand
the
difference
between
the
standard
error
and
the
estimated
standard
 error
 o Understand
when
the
distribution
of
sample
means
will
be
normal
 o Understand
how
the
shape
of
the
distribution
of
sample
means
changes
with
sample
 size
 o Understand
whyit
is
important
for
the
distribution
of
sample
means
to
be
normal
 o Understand
what
the
central
limit
theorem
tells
us
tells
us
about
sampling
 distributions

 
 • 
Hypothesis
testing
in
general
 o Understand
the
basic
logic
of
a
hypothesis
test
 Understand
what
critical
regions
are
in
a
hypothesis
test
 Understand
what
alpha
is
 o Be
able
to
perform
hypothesis
tests
at
alphas
other
than
.05;
what
changes?
 o Be
able
to
use
the
z
and
t
tables
(will
be
provided)

 o Know
what
changes
in
a
one‐tailed
hypothesis
test
 o Know
some
of
the
issues
with
using
a
one‐tailed
hypothesis
test
 o Know
how
to
correctly
interpret
the
result
of
a
hypothesis
test
 o Know
what
a
Type
I
error
is
 Understand
why
the
probability
of
a
false
alarm
is
alpha
ONLY
when
the
null
 hypothesis
is
true
 o Know
what
a
Type
II
error
is
 Understand
why
the
probability
of
a
miss
is
beta
ONLY
when
the
alternative
 hypothesis
is
true
 o Know
why
we
never
“accept”
the
null
hypothesis
 o Know
the
difference
between
statistical
significance
and
practical
significance
 o Be
able
to
calculate
Cohen’s
d,
and
understand
what
it
shows
 o Know
the
differences
between
the
t
and
z
distributions
 o Understand
the
concept
of
degrees
of
freedom,
and
know
how
to
calculate
degrees
of
 freedom
for
a
t‐test
 o Understand
the
concept
of
power
 Know
what
factors
can
influence
the
power
for
a
test
 Know
how
to
compute
power
for
a
z
test
 o Understand
what
p‐values
tell
us
 o Understand
why
the
p‐value
is
not
the
probability
that
the
null
hypothesis
is
true
 
 • Hypothesis
Testing:
Tests
to
know
 o Be
able
to
perform
a
hypothesis
test
from
raw
data,
or
from
summary
statistics
(for
 example,
if
I
were
to
give
you
the
mean
and
standard
deviation
of
a
sample,
and
μ
for
 a
population,
be
able
to
compute
the
correct
hypothesis
test)
 o Know
how
to
perform
a
hypothesis
test
when
σ
and
μ
are
known,
z‐test
 o Know
how
to
perform
a
hypothesis
test
when
σ
is
unknown
but
μ
is
known,
one
 sample
t‐test
 o Know
how
to
perform
a
hypothesis
test
when
σ
and
μ
are
unknown,
independent
or
 dependent
sample
t‐tests
depending
on
study
design
 o Know
which
tests
are
applicable
for
which
experimental
situations,
especially
when
 to
use
a
dependent
vs.
independent
samples
t‐test
 
 
 Some
Important
Formulas
from
Before
Midterm
1
 
 Mean
 Sum
of
squares
for
sample
 
 








 
 
 
 
 
 Standard
deviation
for
population
 From
SS
 
 
 
 
 Standard
deviation
for
sample
 From
SS
 
 
 
 
 Sum
of
squares
for
population,

 Definition
form
 
 
 
 
 Sum
of
squares
for
population
 Computational
Form
 
 
 
 
 
 
 
 Sum
of
squares
for
sample
 Computational
Form
 
 
 
 
 Z­score
to
raw
score
 
 
 Raw
score
to
z­score
 
 
 
 
 
 
 
 
 
 
 
 Some
Important
Formulas
from
After
Midterm
1
 
 
 
 
 Single
Sample
z­test:
 test
statistic
 Single
Sample
t­test:
 sample
variance
 
 X−µ zX = σX SS s= df 
 2 
 
 
 
 Single
Sample
z­test:
 standard
error
 σ2 σX = n Estimated Cohen's d = X−µ tX = sX Single
Sample
t­test:
 
estimated
standard
error
 sX = 
 
 X−µ s Dependent
samples
t­test:

 test
statistic:
 t XD XD = sXD 
 Dependent
samples
t­test:
 estimated
standard
error
 sXD = 2 s n 
 
 Single
Sample
t­test:

 test
statistic
 
 
 Single
Sample
t­test:
 estimated
Cohen’s
d

 
 2 D s n 
 
 
 
 
 Dependent
samples
t­test:

 sample
variance:
 Independent
samples
t­test:

 Pooled
variance
 
 SSD s= df 2 D SS1 + SS2 s= df1 + df2 2 p 
 Independent
samples
t­test:
estimated
 standard
error
 SSD = Σ( X Di − X D )2 
 Dependent
samples
t­test:

 sample
SS
computational
formula
 2 SSD = ΣX 
 Estimated Cohen's d = XD 2 sD Independent
samples
t­test:

 test
statistic
 
 (X1 − X2 ) = s(X1 − X2 ) 
 
 n2 
 X1 − X2 s2 p 
 
 
 Power
Calculation
Formulas
for
z­tests
 
 Critical
value
of
the
mean:
 
 
 
 t(X1 − X2 ) s2 p Independent
samples:
estimated
Cohen’s
d

 
 
 Dependent
samples:

 estimated
Cohen’s
d

 
 Estimated Cohen's d = n1 + 
 
 
 Di n s2 p s( X1 − X2 ) = 
 ( ΣX ) − 
 
 Dependent
samples
t­test:

 sample
SS
definitional
formula
 
 2 Di 
 
 Xcritical = µnull + zcriticalσ X 
 
 
 z
relative
to
distribution
of
z
under
the
 alternative
hypothesis:
 
 Xcritical − µalternative z= σX 
 
 
 
 ...
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This note was uploaded on 10/16/2011 for the course PSYCH 60 taught by Professor Parris during the Spring '11 term at UCSD.

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