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FALL 2022 > Psych Stats > Flashcards

Psych Stats Flashcards

1
Q

Descriptive Statistics

A

summarizes or describes a set of data with numbers (means, standard deviation, median etc.)

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2
Q

Inferential Statistics

A

The conclusions you draw from numbers/the descriptive stats - inferences about the world

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3
Q

Independent Variable

A
  • what you are manipulating
  • on the x axis
  • want this to be the only thing different between groups
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4
Q

Dependent variable

A
  • what is measured (the outcome)
  • the effect
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5
Q

Continuous Variable

A

not set categories; full range of values
examples: height, skin tone, weight

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6
Q

Discrete Variables

A

Specified values, whole numbers, yes or no, categories
Examples: yes/no, gender, colors,

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7
Q

Nominal

A

A category or name; always discrete
ex: colors, majors, types of sleep (REM, Alpha 1 etc).

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8
Q

Ordinal

A

Rankings of things: but may not be evenly numerically spaced (always discrete)
ex: positions in a race, levels of emotion

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9
Q

Interval

A
  • Equally spaced rankings (continuous)
  • can not multiply or divide them
  • no meaningful zero
    ex: IQ test, celsius or fahrenheit
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10
Q

Ratio

A
  • no negative numbers
  • meaningful zero
  • often continuous
    ex: height, time, length
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11
Q

Meaningful Zero

A

when 0 means the absence of the thing being measured (even if not attainable)
ex: 0 Kelvin is no temperature, 0 feet is no feet tall

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12
Q

Between Group Designs

A

Different people in different conditions – comparisons between groups

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13
Q

In Group Designs

A

All people do both conditions: comparisons are made within the same groups

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14
Q

Correlation Studies (NonExperimental)

A
  • Methods: surveys, observations
  • No manipulation of the independent variable
  • No random assignment
  • find associations not causation (correlation does not = causation)
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15
Q

Experiments

A
  • random assignment
  • manipulated IV and controls
  • once variable measured (DV)
  • purpose: to establish cause and effect – able to make causal statements
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16
Q

Extraneous variable

A

an outside variable that may influence the dependent variable

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17
Q

Operational Definitions

A

How we choose to measure or manipulate variables of interest

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18
Q

Measures of Central Tendency

A
  1. Mean
  2. Median
  3. Mode
19
Q

Mean

A
  • average of the data points
  • add all up and divide by number of data points
  • evenly balances deviations (data points add up to the same number on both sides)
  • HIGHLY affected by outliers
20
Q

Median

A
  • the middle score in a data set
  • number of points +1 / 2 to find the median number (not value but place in data set)
  • not very effected by outliers
21
Q

Mode

A
  • What occurs most frequently in a data set
  • highest bar in a histogram
  • may not be a mode in a data set
  • may be more than one mode
  • not effected by outliers
22
Q

Variability

A

How the data varies in a set

23
Q

Range

A
  • The min score to the max score
  • heavily affected by outliers
24
Q

IQR

A
  • 75th percentile minus the 25th percentile
  • the middle 50% of the data
  • not as affected by outliers
25
Q

Variance

A

Formula: sum of (a data point - the mean) squared – all divided by the number of data points

26
Q

Standard Deviation (SAMPLE)

A

Square root of Variance

Sqrt of : sum of (a data point - the mean) squared – all divided by the number of data points

27
Q

Standard Deviation (POPULATION)

A

Formula: sqrt ( sum of (a data point - the mean) squared – all divided by the number of data points + 1)
- used for inferential statistics

28
Q

Population

A
  • The group of people on the whole you are interested in studying
  • mean is called muew (long u)
  • results: parameters
29
Q

Sample

A
  • the group you are actually studying (from the population of interest)
  • mean is X with a line over it or M
  • Results: called statistics
  • Sample mean: good, unbiased estimator of underlying population mean
30
Q

Effect Size (EDIT)

A
  • indicates the magnitude of an effect
  • units: ???
31
Q

Cohen’s d

A

tells us how two distributions overlap
- small effect: d= 0.2
- medium effect: 5= 0.5
- large effect: d = 0.8
report as positive
mean difference between groups

32
Q

Point Estimate

A

Something we get from a data set (estimating population mean from sample mean)
- always in the middle of the confidence interval: has error bars on either side

33
Q

Interval estimate

A

range of plausible values of the population parameter

34
Q

confidence intervals

A

allows you to infer something about the mean of an unmeasured population
- smaller is better: more precise estimate
- can be used for 1) mean 2) standard deviation 3) difference between means of two populations 4) effect size

35
Q

Line Graphs

A
  • imply continuous data
  • Scale IV and DV (interval, ratio)
  • often time series: measurements taken over regular time intervals
36
Q

Scatterplot

A
  • 2 scale variables
  • each dot is a data point
  • specific to correlational / regression designs
37
Q

Bar Graphs

A
  • Nominal or Ordinal IV (category on x-axis)
  • Scale DV (number on y-axis)
  • Good to show differences between means
38
Q

Normal Distribution

A

symmetrical bell curve
- unimodal (one peak) – half data above and below it

39
Q

Bi Modal Distribution

A

more than one mode (2+ peaks)

40
Q

Positive skew

A

Clump towards the bottom
- floor effect: everyone gets low measurement

41
Q

Negative Skew

A

Clump towards top
- ceiling effect: everyone close to top of measurement scale

42
Q

Skew Affects

A

positive:
- higher mean, slightly higher mode
negative:
- lower mean, slightly lower mode

43
Q

Outlier

A

An extreme score

FALL 2022 (11 decks)

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