This is a visual summary based on the lectures of the Statistics 1 course for the English Psychology track at the VU. All concepts that are important for the exam and that have been discussed during the first 5 weeks are summarized and further visually explained using the examples given in the lect...
② Place on the measurement scale
discrete
categorical &
• Qualitative ( categorical ) → -0
Me -0 discrete
-0 Continuous or discrete
to
•
Quantitative ( numerical )
!
→
③ Range
• Discrete = measure unit is indivisible (siblings ) . . . . .
•
Continuous unit is divisible ( height)
-
=
measure
, The quality of an inferential statistic depends on how representative the sample is of the population
-0 so
you
need a random
sample taken from
your sampling frame f- list of all subjects in the
population )
Using random numbers ( =
computer generated selection )
Sampling methods
↳ simple Random sampling choosing random difficult
=
assigning everyone a number & numbers -0
'
↳ systematic sampling =
e.g .
Choosing every 4th person in a Room , using a skip number
'
↳ Cluster sampling =
choosing a few clusters within a
population leg . 100/360 high schools )
(strata )
↳ stratified Random sampling from
=
Selecting participants particular demographic categories in a
way that is proportionate to their membership of the population
↳ from
multistage sampling =
choosing a Random cluster ether
randomly selecting individuals it
What sample to Use depends on • the composition of the target population
-
1 .
Natural variation between samples ( is why we use a
margin 01 error )
2 .
Problems / mistakes with the sample
••
Sampling error = natural sampling variation
Sampling ( non probability sampling
•
bias = Selective sampling e.g .
Volunteer sampling)
,
or under
coverage ⇐ lacking representation of certain population groups )
•
Response bias = incorrect answering by respondents (e.g .
yea saying ) or bad
question wording
••
Non response bias Selective bc be refuse to
=
participation some con 't reached or participate
-
Descriptive Statistics methods
In describing data ,
3 dimensions are important
→
⑦ Central tendency (e.g .
mean
,
median ,
mode ) -0 the mean is not a
good central tendency when there a re
many
outliers !
⑦ Spread / dispersion / variability ( e.g .
Standard deviation ) -0 a mean can be similar for two curves but the spread can differ !
③ Position ( e.g .
on the axis ) -0
you can look at
quartiles or percentiles of interest
Jar
(
fi!saijivgelamfregyency
tin )
:p: not
buttons counts or % distribution stemmata
'
,
Central tendency measure Mode (Weighted ) average (mean ) median mode
fin
, ,
✓ = 1 -
Dispersion measure Variance ratio N
Range standard deviation
,
inter quartile range
/
,
! /
,
-
Position measure
percentile quartile
, ,
minimax ,
median
,
2 -
score CSP from
-
mean
#
entre.spreaitioioefgureboxpot-ocaseswil.tn
→
values > 3x IQR
Calculationsfortheboxplot
with values between i. s 3 x IQR / QR = Q3 -
Qi
cases
-
→
I lowest values no greater
lower limit = Q1 1.5 x IQR = lower wisher limit
highest
-
-0 extend to the
the ' QR
than -5 ×
Q3 IQR limit
'
* mean
wisher
upper limit = t 1.5 x
upper
=
←
line median
-
✓ To box = inter quartile range
thus 50%01 observations
-0 does not mean the wisher extends up to there
( top ,
-
but to the last nr .
within the limit
, skewed right skewed left
whatfigureretochoosedepadsono.tk
scale of the variable ( qualitative or quantitative )
•
Skewness of the distribution
•
Outliers in the data
Standard deviation s of n observations is
S=✓EnG
-
-
which means s=Fm%¥ts→ Because we first square each deviation & then sun those squares .
Sample size 1
It's
wrong to first add deviations
-
together & then square them
↳
q reason for n - e is
Variance = S2
discussed in ch -
5
Week 2 : chapter 4 & 5
Probability The
=
probability of an outcome is the
proportion of times that outcome would occur in a
very long
So
long frequency
'
of it's relative
'
sequence observations -• a -
run distribution
Basicprobabilit-y.us
• P (A) -0 notation of probability of outcome A
p (not A) Pla ) that
Probability
•
= 1 -
-0 outcome A does not occur
•
PCA or B) = PH) t PCB ) -
D
probability of outcome A OR
Ag
B
•
P (A and B) = PCA ) x PCB gives A ) - D probability that booth A IB will occur when B is defeat on A
•
P (A and B) = Pla ) x PCB) →
probability that both A- & B will occur when both independent
-
-
Probability distribution = lists possible outcomes & their probabilities
£8 For discrete variables :
you assign a
probability for each possible value of the variable , using a p between o -
T
and everything together adding up to 7
e g -
.
ideal hr .
Of children
|#B For continuous variables : you assign probabilities to intervals of numbers -
b
you then can tell the
probability that
the
a variable will fall in a particular interval using the areas of probability under curve
, teare3typesofdistributions(ofprobabilit#
⑦ The
population distribution statement of the frequency with the
=
a which units of make
analysis up a
population
are Expected to be) observed in the various categories that make up a variable
-8 often unknown
TBA parameters : M mean
o standard deviation
N population size
② The sample distribution = a statement of the frequency with which the units 01
analysis make up a
sample
-
are Expected to be) observed in the various categories that make up a variable
-
Bo should look similar to the population distribution
poor statistics : I mean
s standard deviation
sample size
\
n
③ The sampling distribution = a statement oh the frequency with which values of statistic s are (expected to be ) observed
when a number of random samples are drawn from a
given population
BB specifies the probabilities for the possible values the statistic can take (due to natural
variation)
to describes statistic across samples :
MJ mean
,
will equal M (or tested )
standard deviation Standard
og =
error
←
sampling
D infinite samples of size n
population
y
Centimeter if you take sufficiently large samples from the population with
c-
£TdTFerds on
sample
replacement then the sampling distribution of
sample means will be size
approximately a normal distribution
TB We
generally view the mean of the
sampling distribution as the
population mean so Mg =
M
IB The standard deviation be the standard sample
of the
sampling distribution can seen as error of drawing a
from that particular population so : og = Fin -0 aka . dependent on size of sample taker
,
bigger sample = smaller standard er ror
Toda No the distribution the be
matter the shape of
population , sampling distribution will
normally distributed .
This normality property is used for significance & constructing confidence
testing intervals
to
Karge sample becomes more important when the population distribution is
relatively skewed (for validity )
The benefits of buying summaries with Stuvia:
Guaranteed quality through customer reviews
Stuvia customers have reviewed more than 700,000 summaries. This how you know that you are buying the best documents.
Quick and easy check-out
You can quickly pay through credit card or Stuvia-credit for the summaries. There is no membership needed.
Focus on what matters
Your fellow students write the study notes themselves, which is why the documents are always reliable and up-to-date. This ensures you quickly get to the core!
Frequently asked questions
What do I get when I buy this document?
You get a PDF, available immediately after your purchase. The purchased document is accessible anytime, anywhere and indefinitely through your profile.
Satisfaction guarantee: how does it work?
Our satisfaction guarantee ensures that you always find a study document that suits you well. You fill out a form, and our customer service team takes care of the rest.
Who am I buying these notes from?
Stuvia is a marketplace, so you are not buying this document from us, but from seller FabienneDenberg. Stuvia facilitates payment to the seller.
Will I be stuck with a subscription?
No, you only buy these notes for $7.56. You're not tied to anything after your purchase.
