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Premade Weekly Decks > STATS T1 > Flashcards

STATS T1 Flashcards

1
Q

Categorical variable

A

Variable with scores that are not on a numeric scale

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2
Q

Descriptive statistics –

A

Summarise samples – giving someone the main points in a simple form To describe data, we will use graphical and numerical (statistical) techniques

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3
Q

Inferential statistics –

A

Examine patterns in the data and consider how much data we have You can then draw conclusions about a population based on the analysis of a sample. -> conceptual replication

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4
Q

Summarising

A

collecting and summarising data

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5
Q

Statistical inference

A

the ability to draw general conclusions from samples

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6
Q

How many times does a particular score occur?

A

Percentages/Averages Scores for a particular variable (Frequency statement)

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7
Q

Do scores for one variable correlate with scores for the other variable?

A

Statement about association

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8
Q

How strong is the correlation or association between two variables?

A

Statement about association

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9
Q

Do I trust that there is a “genuine” association (relationship)?

A

Statement about relationship between two variables

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10
Q

Frequency Distribution?

A

show scores in order and their frequency of appearance in the sample

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11
Q

Negatively skewed

A
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12
Q

Positively Skewed

A
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13
Q

When not to describe the skew of data?

A

When we cannot put our scores in order , from lowest
to highest so when we are describing a categorical
variable with unordered categories

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14
Q

Unimodal?

A

One major peak

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15
Q

Bimodal

A

Two major peaks

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16
Q
A

Approximately symmetrical

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17
Q

How do outliers and the mean relate to each other?

A

Outliers are extreme values that differ from most values in the data set. Because all values are used in the calculation of the mean, an outlier can have a dramatic effect on the mean by pulling the mean away from the majority of the values.

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18
Q

What happens to the mean, median and mode in a skewed distribution

A

in normal distributions, they all take on the same number

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19
Q

Why are histograms good?

A

effective visual summary of a variable’s central tendency and variability

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20
Q

What is a discrete, continuous, independent and dependent variable?

A

Discrete: variable that is limited (age, gender) Continuous: exists on a continuum basically infinite between highest/lowest IV: variable manipulated/changed to see whether it has an effect on the DV that might change because of the manipulation DV: variable that, though measured, is not being controlled

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21
Q

What is the role of measurement scales?

A

The numbers don’t necessarily say anything concrete about the objects measured <i>ex.: if I scored high on a test, but someone else scored lower, it’s not necessary because they remembered less even though the data might suggest it → we assume that they mean I remembered more</i>

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22
Q

What is the purpose of a frequency distribution?

A

Organising data into a meaningful order of how many times

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23
Q

Which variable do I usually find on the X- and Y-Axis in histograms vs. line graphs?

A

histogram: dv-iv Line/Bar graph: iv-dv

24
Q

What is the mode, median, mean (+formulas)?

A

Mode: the highest point in the graph Median: 50th percentileMean: Sum of N/ N

25
Q

If the mean is slightly larger what does it probably say about or distribution?

A

Positively skewed

26
Q

When will the mean and the median be equal?

A

Symmetric distribution

27
Q

The benefit of the mode is?

A
  • Representing categorical data * More informative *But not very reflective of the remaining data set
28
Q

The benefit of the median is?

A

Not affected by outliers Not stable in comparison and not useful to calculation

29
Q

What does central tendency refer to?

A

The scores tendency to distribute in a certain way?

30
Q

What is the advantage of a bar chart?

A
  • Comparing categories * Mirrors other visualisation techniques were the spread is along the X-axis and the frequency or percentage is along the Y-axis already hints at modality and skewness
31
Q

What is an alternate name for the y-axis/x-axis?

A

ordinate/abscissa

32
Q

Suppose you sell ice cream with three different flavours: chocolate, strawberry and yogurt. The ice cream flavours are measured on a ____________ level. You sell ice cream to children, adults and elderly people. These age groups are measured on a ____________ level.

A

nominal; ordinal

33
Q

operational definition

A

defining a variable in terms of the set of steps or procedures that the researcher goes through in order to manipulate or measure the variable

34
Q

right skewed

A

positively skewed

35
Q

What does a negatively skewed distribution reveal?

A

A lot of people got close to the maximum score

36
Q

What does central tendency mean?

A

average score

37
Q

Age in months is an example of a variable with a ratio scale of measurement. Select one:
True
False

A

T

38
Q

What are two ways to visually represent to measurement data variables?

A
  1. scatter plots 2. contingency tables/crosstabulation
39
Q

What is a way to visually represent a mix of categorical and measurement data?

A

compound histogram

40
Q

What is a way to visually represent categorical data pairs?

A

crosstabulation

41
Q

What are the groupings of scores in histograms called?

A

bins

42
Q

Do these images show the same data?

A

Yes

43
Q

Which visual representation should you choose if you want to show that variables vary simuntaneously?

A

scatter-plots

44
Q

What does a boxplot do?

A

summarises the data while showing the range, interquartile range, as well as the min, max and the median

45
Q

When is the mean most useful?

A

best for interval/ratio measurement data (categorical data can hardly be split into 2), needs equal spacing between adjacent values

46
Q

What is the mode most useful for?

A

all but notably for nominal/ordinal categorical data because popular choice

47
Q

Variables are

A

properties of objects that vary in the values that they take on

48
Q

A score is

A

an individual value for a variable

49
Q

Measurement data describes

A

scores on a numerical scale

50
Q

Categorical data describes

A

scores not on a numerical scale

51
Q

A Population describes

A

a complete set of scores that might be of interest

52
Q

A Sample is

A

a sub-set of scores from a population which were obtained

53
Q

A parameter is

A

a number that summarises the entire set of scores in a population

54
Q

A statistic is

A

a number that summarises the scores in a sample

55
Q

Descriptive Statistics…

A

summarise samples by presenting the main points in a simplified way

56
Q

Inferential statistics…

A

examine patterns in the data and consider the amount of data

57
Q

Ethnicity or political ideology are examples

A

nominal variables

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