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Psychology > PS1018 - statistics > Flashcards

PS1018 - statistics Flashcards

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1
Q

why do psychologists need statistics

A

to summarise/describe data
to generalise from samples to populations

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2
Q

variables

A

anything that can have different values

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3
Q

discrete variables

A

limited number of values, countable values

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4
Q

continuous variables

A

uncountable, infinite data

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5
Q

categorical data

A

nominal and ordinal

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6
Q

numerical data

A

interval and ratio

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7
Q

nominal

A
  • no rank/order
  • mode most common measure of CT
  • Gender/eye colour
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8
Q

ordinal

A
  • categories, but has an order/rank
  • median most common measure of CT
  • level of agreement
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9
Q

interval

A
  • usually measured in numbers
  • have an order, spaces between measurement are equal
  • mean most common measure of CT
  • temperature
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10
Q

ratio

A
  • ordered/ranked
  • distance between points is consistent
  • zero point = absolute zero
  • mean most common measure of CT
  • mean most common
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11
Q

frequency histograms

A

graphical representation of distribution of a data set

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12
Q

unimodal distributions

A

only one most common score

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13
Q

bimodal distributions

A

two equally common scores

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14
Q

positively skewed

A

tail goes towards the negative end

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15
Q

central tendency

A

where most of the scores are

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16
Q

variability

A

degree of ‘spread’ about an average

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17
Q

interquartile range

A
  • find the median
  • find first quartile (middle score of lower half of scores)
  • find third quartile (middle score of upper half of scores)
  • difference between 1st and 3rd quartiles
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18
Q

s2

A

sample variance

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19
Q

sample variance - xi

A

term in data set

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20
Q

sample variance - x- (line on top of x)

A

sample mean

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21
Q

sample variance - n

A

sample size

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22
Q

how to calculate sample variance

A
  • calculate the mean
  • subtract mean from each data value
  • square the results
  • add results together
  • divide this result by n-1
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23
Q

example data : 2, 6, 8, 3, 5, 7, 2, 1, 2

A

s2 = 6.5

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24
Q

sample standard deviation

A

square root of sample variance

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25
Q

problems with summary statistics

A

hides info about full distribution
doesn’t represent whole data set
‘ignores’ information about individual differences

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26
Q

what is sample variance

A

measures how much the data points in a sample deviate from the sample mean

27
Q

Standard Error of Mean (SEM)

A

SD/square root of sample size

28
Q

the raincloud plot

A

most rich data representation, showing individual data and distribution

29
Q

raincloud plot - the cloud

A

smoothed representation of distribution

30
Q

raincloud plot - summary plot

A

the mean

31
Q

raincloud plot - the ‘rain’

A

individual data

32
Q

what does a boxplot show

A

median
1st and 3rd quartile
error bars - range of data within 1.5 IQR of 1st and 3rd quartiles

33
Q

why use boxplots

A
  • skew and outliers show
  • richer than simple summary stats
34
Q

histograms

A

show full distribution of data

35
Q

probability of an event

A

number of possible occurrences of the event divided by the total number of all possible events

36
Q

probability rules - addition rule (A or B)

A

P(A or B) = p(A) + p(B)
A & B are mutually exclusive (if A occurs, B cannot)

37
Q

probability rules - multiplication rule (A and B)

A

p(A,B) = p(A) x p(B)
A & B are independent - A occurring doesn’t effect B occurring

38
Q

Example: Bag of 100 marbles, containing 10 red, 30 green, 60 blue
- probability of red

A

10/100 = 0.1 (10%)

39
Q

Example: Bag of 100 marbles, containing 10 red, 30 green, 60 blue
- probability of green

A

30/100 = 0.3 (30%)

40
Q

Example: Bag of 100 marbles, containing 10 red, 30 green, 60 blue
- probability of blue

A

60/100 = 0.6 (60%)

41
Q

Example: Bag of 100 marbles, containing 10 red, 30 green, 60 blue
- probability of either a red or blue marble in 1 pick

A

p(r o b) = p(r) + p(b) = 0.7 (70%)

42
Q

Example: Bag of 100 marbles, containing 10 red, 30 green, 60 blue
- probability of red marble on 1st pick, then blue marble on 2nd pick (return marble after 1st pick)

A

p(r,b) = p(r) x p(b) = 0.1 x 0.6 = 0.006 (6%)

43
Q

What is the sign test

A

non-parametrical statistical test

44
Q

what is the purpose of the sign test

A

to determine whether there is a significant difference between medians of two related groups

45
Q

when should you use the sign test

A
  • data is paired of matched
  • data is ordinal or does not meet parametric assumptions
  • testing for a median difference
46
Q

sign test - step 1

A

calculate the difference between paired observations

47
Q

sign test - step 2

A

assign a sign to each difference (+, -, 0)

48
Q

sign test - step 3

A

ignore cases with no differences

49
Q

sign test - step 4

A

count the number of positive and negative signs

50
Q

sign test - step 5

A

use the binomial distribution to test the null hypothesis

51
Q

what is the test statistic in the sign test?

A

the smaller of the counts of positive or negative signs

52
Q

how to determine if the results are significant in a sign test

A
  • compare the test statistic from the critical value from the binomial distribution table
  • if its less or equal to critical value, reject null hypothesis
53
Q

advantages of the sign test

A
  • simple to use
  • doesn’t require normally distributed data
  • suitable for small sample sizes
54
Q

limitations of the sign test

A
  • only considers the direction of change, ignoring magnitude
  • tied values (zero differences) are excluded, may reduce sample size
55
Q

Wilcoxon matched-pairs test

A
  • within-subjects test of differences
  • two condition experiments with ordinal data
  • non-parametric
56
Q

What variables do difference tests include?

A

Discrete and continuous

57
Q

What variables do relationship tests include?

A

Two continuous variables

58
Q

Non-parametric test

A

data doesn’t have to be normally distributed

59
Q

Parametric tests

A

data is normally distributed

60
Q

why use Wilcoxon instead of sign test when possible?

A

Sign test ‘throws away’ size of differences, but Wilcoxon is sensitive to this

61
Q

what test to use with two categorical variables

A

chi-square

62
Q

what test to use with one numerical variable

A

t-test

63
Q

what test to use with one numerical and one categorical variable

A

t-test or ANOVA

64
Q
A

Psychology (8 decks)

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