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PSYC 305 > Midterm #1 > Flashcards

Midterm #1 Flashcards

1
Q

Statistical Origins

A

began 100-120 years ago with 4 guys in England

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2
Q

Statistical Origins

  • **4 guys (100-120 **years ago in England)
A

**Francis Galton **

Karl Pearson

Ronald Fisher

William “student” Gossett

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3
Q

Francis Galton

A

interested in **quantifying human variation **

  • money man *
  • eugenics *
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4
Q

Karl Pearson

A

wanted to show relationships between variables

  • student of Galton** *
  • fan of **Karl Marx ***
  • enemy = **Ronald Fisher ***
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5
Q

Ronald Fisher

A

wanted to **test if something caused something **

  • statistics & genetics *
  • studied causal relationships *
  • enemy: Karl Pearson*
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6
Q

William “student” Gossett

A

just wanted **everyone to get aloing **

*worked at brewery *

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7
Q

Psychology & **Statistics **

  • ​history/prevalence within psychology
    *
A

When **Freudians & Behaviorists **ruled psych → no need for stats

**Personality, social, cognitive **psychologists created **demand **for statistics

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8
Q
  1. stats became… when? (2)
  2. debate (2), when?
A

→ became language of psychology in 1950s

→ 1980s: stats became more complex (computer rev.)

21st Century - debate **(quantitative vs. qualitative) **

  • bigger debate around how we use stats
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9
Q

Definition of Statistics (2)

A

**Statistics **as:

  • **collection **of **numerical facts **
  • **methods **for dealing with **data **
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10
Q

(2) Types of Statistics

A

1) Descriptive
2) **Inferential **

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11
Q

**Inferential **statistics allow us to?

A

generalize from **samples **to **population **

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12
Q

Population

A

complete set of **individuals, objects **or **measurements **having some common characteristic

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13
Q

Parameter

A

any **characteristic **of a population that is measurable

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14
Q

Sample

A

**subset **of a population

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15
Q

Statistic

A

**number **resulting from **manipulation **of sample data

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16
Q

Scales **(4) **

A

NOIR

Nominal

Ordinal

Interval

Ratio

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17
Q

Nominal Scale

A

observation of **unordered variables **with **no ranking **to be inferred

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18
Q

Ordinal Scale

A

classes differ & indicate rank

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19
Q

Interval Scale

A

classes differ in **meaningful way **so arithmetic operations are possible

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20
Q

Ratio Scale

A

interval scale but with **meaningful zero point **

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21
Q

Grouping

A

**collapsing **scores into mutually exclusive classes defined by **grouping intervals **

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22
Q

Grouping Data

**- pros (3) **

A
  • difficult to deal w/ large # of cases spread over many scores
  • some scores have low frequency counts
  • less data leads to greater comprehension
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23
Q

Grouping Data

  • **cons (2) **
A
  • info is lost when categories/data are combined
  • categories can be **arbitrary **
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24
Q

Ungrouped Frequency Distribution

A

frequency distribution (table that displays frequency of various outcomes in a sample) that does NOT group data into intervals

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25
Q

Grouped Frequency Distribution

A

groups data into intervals of size i

  • mutually exclusive & exhaustive

frequency is equal to the number of values that fall within this interval

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26
Q

Cumulative Frequency Distribution

A

also include cumulative frequency (cf ), which indicates the number of values within the specified interval + # of values previously counted

ON GRAPH: highest point reached is total (n) # of values

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27
Q

Cumulative Percentage Distribution

A

also includes c%, which is cf/n x 100%

  • shows the cumulative frequency as a percentage of the total (n) # of values

ON GRAPH: highest point reached is 100%

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28
Q

IQ scores would be an example of data that are?

A

Interval

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29
Q

Percentile Ranks

A

form of cumulative percentage that indicate where scores fall in a distribution

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30
Q

How do percentile ranks work?

  • i.e. PR = 10%
A

a score with a PR = 10% indicates that:

  • its value is greater than 10% of all scores
  • its value is less than 90% of all scores
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31
Q

Central Tendency

A

index of central location employed in the description of a frequency distribution

32
Q

Mean

A

average taken by summing scores & dividing sum by # of scores

  • point in a distribution about which summed deviations are equal to zero → Σ(x-bar - x) = 0
33
Q

Mean **formula **

A

sample: x-bar = Σx/n
population: µ = Σx/N

34
Q

Deviation score

A

score minus mean

x - (x-bar)

→ summed deviations from the mean = 0

35
Q

Sum of Square Deviation Scores

  • aka?
  • size?
  • positive/negative?
A

aka SUM of SQUARES (SS)

  • never negative
  • SS from the mean are LESS than SS from any other number
36
Q

If data is from population rather than sample?

A

use N instead of n → # of scores

μ instead of x-bar → mean

37
Q

Median

A

score that divides distribution so that same # of scores lie on each side

38
Q

Median is a special case of?

A

percentile rank (50th percentile)

39
Q

Mode

A

score that occurs with greatest frequency

40
Q

____ is associated with ___ data

a) Mode
b) Median
c) Mean

A

a) nominal data
b) ordinal data
c) interval/ratio data

41
Q

If data distribution is normal…

mean, median & mode are…

A

same value

42
Q

If distribution of scores is NOT normal…

mean, median & mode….

A

fall alphabetically from tail

1) mean
2) median
3) mode

43
Q

Non-normal distributions may be..

A

skewed positively or negatively

44
Q

Which distributions have kurtosis?

A

distributions that are too light or heavy in the tails have kurtosis.

45
Q

Do

  • a) distributions with kurtosis*
  • b) skewed distribution*

affect central tendency?

A

a) NO
b) YES

46
Q

A score at the median is at the ____ percentile

A

50th

assuming normal distribution

47
Q

Variability

A

the dispersion of scores in a distribution

48
Q

range

A

crude measure easily influenced by outliers

49
Q

semi-interquartile range

A

less influenced by outliers but still crude

(75th - 25th) / 2

50
Q

Standard Deviation & Variance (3)

  • reflect…
  • basis for…
  • exploits…
A
  • reflect dispersion of scores
  • basis for all inferential statistics
  • exploits mean as best measure of central tendency
51
Q

Variance

A

quantitative measure of difference between scores in a distribution that describes degree to which scores are spread out/clustered together

52
Q

Variance formula

A

S2 = SS/(n-1)

= Σ(x-x̄)2 /(n-1)

53
Q

Standard Deviation

A

square root of variance

provides measure of standard/average distance from mean

54
Q

Standard Deviation formula

A

S=√S2

=√ [Σ(x-x̄)2 /(n-1)]

55
Q

Deviation **Method **formula

A

if you have **many **scores

SS = ∑x2 - (∑x)2/n

56
Q

Z score

A

statistical measurement of a score’s relationship to the mean in a group of scores

57
Q

Z score formula

A

Z = x-µ/σ

58
Q

How are **Z scores **useful when given scores from different normal distributions?

A

can find the **z score **of the scores in order to facilitate comparison

59
Q

Z formula is the ___ for many ___ ___

A

foundation for many inferential statistics

60
Q

Z formula **numerator (x-µ) **reflects?

A

how score **deviates **from pop’n parameter (µ)

61
Q

Z formula **denominator (σ) **reflects?

A

**variability **of scores in pop’n

62
Q

the z formula **ratio **represents?

A

score **(z) **that can be compared to theoretical distribution (normal distribution)

63
Q

When using **z scores **for a sample rather than individual score…

A

µx-bar= µ of popn

σx-bar ≠ σ

σx-bar = σ/√n

64
Q

When variables are not normally distributed..

A

Central Limit Theorem to address these issues

  • Zx-bartest
65
Q

Central Limit Theorem

A

the **means **of a large # of independant random samples will be normally distributed regardless of underlying distribution

66
Q

sampling distribution

A

theoretical distribution of possible values of some sample statistic that would occur if all possible samples of fixed size were drawn from a given population

67
Q

If the sampling distribution takes the form of a normal distribution…

A

we can use the known properties of the normal distribution to make inferences

68
Q

Null hypothesis

A

a general statement/default position that there is no relationship between two measured phenomena

69
Q

Alternative hypothesis

A

the hypothesis used in hypothesis testing that is contrary to the null hypothesis.

  • usually taken to be that observations are result of a real effect
70
Q

In psychology, outcome is **unremarkable **if probability of outcome **by chance alone **is?

A

**greater than **5 in 100

(> 5 in 100)

71
Q

Remarkable outcome if probability of occurance by chance alone is …?

A

equal to or less than 5 in 100

(≤ 5 in 100)

72
Q

Type **1 **error

A

α

when you **reject **null hypothesis that is true

73
Q

Type **2 **error

A

β

**failing to reject **null hypothesis that is false

74
Q

Statistical power

A

capacity to find something if its there

75
Q

Logic of Testing

(6) questions to ask

A

1) what is the **appropriate statistic, **its distribution & its assumptions
2) null & alternative hypotheses
3) probability of making type 1 error
4) obtained value for test
5) critical value for test
6) decision regarding obtained value relative to critical value

PSYC 305 (3 decks)

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