Weeks 1-3 Flashcards

1
Q

Population

A

The entire collection of events in which you are interested

E.g. all men, all women, all Deakin students

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2
Q

Sample

A

Subset of the population that is being studied

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3
Q

Parameter

A

Any value we obtain that is characteristic of the population

E.g. the average income of Australian office workers

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4
Q

Descriptive statistics

A

Used to describe the data by summarising, determining averages and ranges.
Makes large amounts of data more manageable.

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5
Q

Inferential statistics

A

Used when we want to answer research questions

I.e. When we infer the behaviour of the population based on the dataset recovered from the sample

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6
Q

The difference between the sample statistic and the corresponding population parameter (because our data will never be 100% accurate)

A

Sampling error

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7
Q

Variable

A

Something that can take on different values

E.g. Age, speed, time

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8
Q

A variable that has a limited number of values

E.g. Gender, set categories

A

Discrete variable

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9
Q

A variable that can take on different valuesE.g. Time, age, IQ

A

Continuous variable

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10
Q

Dependant variable

A

The variable which is observed for differences / changes.
Influenced by the IV.
E.g. Levels of depression in control vs treatment groups

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11
Q

Independant variable

A

The variable which is manipulated by the research.
The IV influences the DV.
E.g. Group membership - participants assigned to either high or low anxiety groups

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12
Q

Measurement data

A

Generally the mean, variance, and standard deviation

E.g. Mean age of students

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13
Q

Categorical data

A

Generally percentages and frequencies

E.g. 25% were female, 12% had black hair

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14
Q

Nominal measurement scale

A

Categories with different names, no underlying scale, and no ordering.
E.g. Religion, hair colour, gender

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15
Q

Ordinal measurement scale

A

Categories with different names and organised into an ordered sequence, however distance between categories is unknown
E.g. Degree of illness (none, mild, moderate, severe)

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16
Q

Interval measurement scale

A

Equal distances between points on the scale.
Generally many more points than on an ordinal scale, usually continuous data.
No true zero point.
E.g. Temperature

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17
Q

Ratio measurement scale

A

Equal distances between points on the scale AND has true zero point.
E.g. Time, length, age

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18
Q

What are the different kinds of measurement scales?

A

Nominal
Ordinal
Interval
Ratio

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19
Q

Frequency distribution

A

How often each score appears on in a dataset.

Can be difficult to determine trends in larger datasets.

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20
Q

Same info as a frequency distribution, but graphically illustrated.

A

Histogram

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21
Q

Stem and leaf plots

A

Can summarise data in a simple way

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22
Q

Normal distribution

A

Most scores in the middle, fewer in the extremes

23
Q

Bi-model distribution

A

When a frequency distribution has two peaks

24
Q

Positive skew

A

Most scores at the low end of the scale

25
Negative skew
Most scores at the high end of the scale
26
Kurtosis
Refers to how flat or peaked the distribution appears
27
Leptokurtic
Distribution characterised by high peak at the centre of the scale
28
Platykurtic
Distribution is flatter, with less scores in the centre
29
Central tendency
The tendency of a random variable to cluster around is mean, median, or mode
30
Variability
How good is the mean as a representation of the data?
31
Low variability
The mean is a good representation of the data
32
High variability
``` The mean is a bad representation of the data. The mean deviates significantly from the data points. E.g. 12 1 78 10 148 Mean = 50 ```
33
Average deviation
1. Calculate the mean 2. Calculate how much each score deviates from the mean 3. Calculate average of the deviation
34
Absolute deviations
When only absolute values are used (remove the negative factor)
35
Variance Represented by? Useful for? Equation?
Measures how far a set of numbers is spread out from their average value Represented by s2 or σ2 Most common measures of variability. Crucial for inferential statistical methods s2 = Σ(x - x̅)2 / N - 1 I.e. sum of the squared deviations from the mean divided by N - 1
36
Standard deviation Equation? Correlation with variance?
Shows how much values differ from the mean. Standard deviation is the square root of the variance. Standard deviation = σ = √[Σ(x - x̅)2 / N - 1] 1. Calculate the deviations 2. Square the deviations to get absolute deviations 3. Sum of the deviations 4. Divide the sum by (n - 1) 5. Square root of remaining value
37
``` Mean = 0 σ = 1 ```
Standard normal distribution
38
Z-scores | Equation?
Indicates how far from the mean a data point is z = (x - x̅) / σ
39
μ
mean of population
40
σ
Standard deviation | 's' commonly used in lieu
41
mean
42
Setting probable limits on z Definition? Use? Equation?
Allows researchers to set limits on a score to establish a certain degree of confidence in their results. Usually employ 95% confidence intervals so that they can say they are 95% confident in their results. x = μ ± z-score x σ
43
Sampling error
Difference in means between population and sample
44
Hypothesis testing
Being able to test our hypothesis to determine whether we discount chance errors in the result or if there is a meaningful result
45
Sampling distributions
Degree of variability between samples we can expect to see by chance. Using sampling distribution we can say how likely it is that we will find a particular sample mean within a population.
46
Standard error
Average distance between a sample mean and a population mean Mean difference / Difference expected by chance (standard error)
47
Hypothesis
Specific, testable predictions
48
Null hypothesis
The hypothesis that there is no difference between certain characteristics in a population. The starting point for any statistical test.
49
Type I error
When we erroneously reject a true null hypothesis | I.e. When we say something is significant, but it isn't
50
Type II error
When we fail to a show that a statistic is significant when it really is
51
One-tailed test
One directionality displayed in a test
52
Two-tailed test
No directionality specific test
53
Directionality
Predicting the direction of difference | E.g. We predict that the mean will be higher than the population