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U1 Psychology Stats 305 > Session 1 > Flashcards

Session 1 Flashcards

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1
Q

what is a Population

A

All entities or individuals of interest

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2
Q

what is the point of a population

A

We often want to learn something about the population

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3
Q

what are Parameters

A

A value that describes the population

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4
Q

what is the symbol for population mean

A

Population mean, μ

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5
Q

what is the symbol for population variance

A

Population variance, σ2

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6
Q

what is Sample

A

A subset of individuals from the population aka Data that we will examine

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7
Q

what does N refer to

A

usually refers to sample size

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8
Q

what is an Estimate

A

A value that describes the sample

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9
Q

what is the symbol for sample mean

A

Sample mean, X (bar on top)

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10
Q

what is the symbol for sample variance

A

Sample variance, s2

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11
Q

what are the 2 types of stats

A

Descriptive Statistics and Inferential Statistics

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12
Q

what is Descriptive Statistics

A

Summarize/describe properties of the sample (or the population if we gather data from the entire population)

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13
Q

what is inferential stats

A

Draw conclusions/inferences regarding the properties of the population, but based only on sample data

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14
Q

what is a Variable

A

A characteristic that varies across observations (people, location, time, etc.)
aka Often a single column in our dataset

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15
Q

wha are Variables also known as

A

levels of measurements

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16
Q

what are the two types of variables

A

Quantitative and Categorical

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17
Q

what are types ofRatio 􏰀

Interval Quantitative variables

A

Ratio 􏰀

Interval

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18
Q

what are the types of Categorical variables

A

Ordinal 􏰀

Nominal

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19
Q

what is Nominal variable

A

Classifies objects

aka Are two observations the same or different on some attribute?
Not quantitative, though we can use numbers to index the categories

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20
Q 
what kind of variables are being used here: 
Which restaurant do you prefer?
Tim Horton’s
Burger King 
Thai Express
A

nominal

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21
Q

Gender
Country of Birth Native Language

these are all examples of what kind of variables

A

nominal

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22
Q

what does Dichotomous (binary) mean

A

Two categories/levels

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23
Q

how many levels in nominal variables

A

2 (Dichotomous (binary))

e.g Treatment/control Correct/incorrect

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24
Q

ranks the variables from highest level of measurement to lowest level of measurement

A

Ratio 􏰀
Interval

Ordinal 􏰀
Nominal

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25
Q

what is Ordinal

A

Rank data

aka Does one observation have more or less of an attribute than a second
observation?
Relative standing of two observations on the attribute Does not say by how much the observations differ

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26
Q 
Birth order (1st, 2nd, 3rd)
Students’ standing in class relative to others 
Importance of personal values

these are examples of what kind of variables

A

ordinal

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27
Q

what is Interval

A

Rating data (equal distances)

aka Assigned numbers have meaningful units, and these unit sizes remain constant

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28
Q

Temperature (when measured using Celsius or Fahrenheit)
Calendar year

these are examples of what kind of variables

A

intervals

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29
Q

what is Ratio

A

Interval, with an absolute 0 point or meaningful origin

aka 0 means lack of the attribute
Comparisons such as “2 times as much” of something or “half as much” make sense

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30
Q

Height (feet, inches, meters), weight, elapsed time, pulse rate, car speed, price ($)

these are examples of what kind of data

A

ratio

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31
Q

what are they 2 types of variable

A 
Independent Variable (IV)
Dependent Variable (DV)
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32
Q

what is Independent Variable (IV)

A

PredictOR (or covariate)

Factors in an experimental design

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33
Q

what is Dependent Variable (DV)

A

Outcome/Response

PredictED variables

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34
Q

what are the Types of Research

A

Correlational vs. Experimental

Between-subjects vs. within-subjects

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35
Q

what is the Correlational IV measured by

A

the researcher

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36
Q

what is Correlational good for

A

Good for ecological validity - generalizing research findings to the real world

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37
Q

what is Correlational not good for

A

Not good for inferring causality; IV and DV may have a relationship due to a third (confounding) variable or common cause

38
Q

what is done to the IV in Experimental

A

IV is manipulated by the researcher

39
Q

what is experimental good for

A

Good for inferring causality

40
Q

what is experiment not good for

A

Manipulating IV in lab settings may sometimes feel detached from the real world

41
Q

Statistical methods to analyze data may be ________ for correlational and experimental designs
Some data examples used in this course may be correlational

A

the same/similar

42
Q

what is Between-subjects design (research)

A

Each participant in only one experimental condition (e.g., control or treatment)

43
Q

with Between-subjects design , If random assignment is used what should happen to the groups

A

groups should be approximately equal on any confounding variables

44
Q

what is Within-subjects design

A

Each participant does more than one experimental conditions (e.g., control and treatment)
aka DV measured multiple times

45
Q

what must researchers be careful about with Within-subjects design

A

Vulerable to practice effects and fatigue/boredom effects as alternative explanations for differences betwen conditions

46
Q

what is Counterbalancing

A

used to help rule out alternative explanations

Counterbalancing is usually thought of as a method for controlling order effects in a repeated measures design (see the notes on variance and experimental design). In a counterbalanced design to control for order effects, we use separate groups of subjects, each group receiving treatments in a different order.

47
Q

what type of research is Counterbalancing used for

A

Within-subjects design

48
Q

In this course, We focus on the relationships between how many IV and DV

A

one/multiple independent variables (IV) and one dependent variable (DV)

49
Q

what is consistent about the DV

A

continuous (usually normally distributed)

50
Q

what is consistent about the IV

A

Categorical/continuous

51
Q

what is the point of Models in statistics

A

Summarize/describe a large amount of data with just a few numbers

52
Q

what is the equation for the stat model according to field

A

Outcomei = (Model) + Errori

53
Q

explain the parts to the model in statistics according to field

A

i subscript used to indicate a participant i
Outcome - the DV
Model - systematic part explained by IV1
Error - unsystematic part that is not explained by IV

54
Q

what are Descriptive Statistics

A

Describe properties of samples (or populations, if completely known)

55
Q

what kind of properties are described with descriptive stats

A

How are the data distributed?
Where is the centre?
How much variability is there in scores? What shape is the distribution?

56
Q

what is used to describe ‘centre

A

central tendency

57
Q

what is in central tendency

A

Mean Median Mode

58
Q

what is used to describe variability

A

variation

59
Q

what is in variation

A

Range
Variance
Standard Deviation

60
Q

what is used to describe shape of distribution

A

shape

61
Q

what is in shape

A

Skewness Kurtosis

62
Q

what is Mean

A

the average

63
Q

The mean is vulernable to what

A

extreme values (outliers)

64
Q

what is Median

A

the value in the middle

65
Q

is the median vulnerable to outliers

A

The median is less vulnerable to extreme values (outliers)

66
Q

what is Mode

A

The value that occurs most frequently

67
Q

is the mode impacted by outliers

A

Not affected by extreme values

68
Q

what is mode used for

A

Used for either numerical or categorical data

69
Q

how many modes might there be

A

There may be no mode

There may be several modes

70
Q

Mean is most commonly used when

A

always unless extreme values (outliers) exist

71
Q

when is the Median used

A

often used if extreme outliers are present

72
Q

when is the Mode used

A

is often used with categorical (nominal) data

73
Q

what are Measures of variation

A

Measures of variation give information on the spread or variability of data values

74
Q

what are the ways to measure variation

A

Range
Variance
Standard deviation

75
Q

what is Range

A

Simplest measure of dispersion

Difference between the largest and the smallest observations:

76
Q

what is Variance

A

(Approximate) average of ‘squared’ deviations of values from the mean

77
Q

what is the equation for sample variance

A

s^2 = SS / N-1 = (Xi −X)^2/N-1

78
Q

explain the parts to the variance equation

A

X = mean
N = sample size
Xi = ith value of the variable X
SS = 􏰁Xi − X)2 = Sum of Squares (sum of squared deviations)

79
Q

what is Standard deviation used for

A

Most commonly used measure of variation

80
Q

what does standard variation show

A

variation about the mean

81
Q

what units does SD have

A

Has same units as the original data

82
Q

how to get the SD

A

Square root of variance

83
Q

what does the Shape of a Distribution explain

A

Also describes how data are distributed Symmetric or skewed

84
Q

what is skewed

A

Which way does the “tail” point?

85
Q

what are the types of skewness

A

left
symmetric
right

86
Q

what is another name for Normal Distribution

A

Gaussian or bell-shaped

87
Q

In many statistical techniques for experimental designs, the dependent variable is assumed to be what

A

continous and Normally distributed

88
Q

if normally distributed, what is true about the mean, median and mode

A

Mean = Median = Mode

89
Q

if normally distributed, what is true about the mean and SD

A

Mean (μ) and Standard deviation (σ) are sufficient to describe a normal distribution

90
Q

If the data distribution is normal, then the interval is what

A

μ ± σ contains about 68% about of the values in the population (or sample

91
Q

what does μ ± 2σ mean

A

contains about 95% of the values in the population (or sample)

92
Q

what does μ ± 3σ mean

A

contains about 99.7% of the values in the population (or sample)

U1 Psychology Stats 305 (21 decks)

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