Weeks 1-3

Question 1

Population

Answer 1

The entire collection of events in which you are interested

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

Question 2

Sample

Answer 2

Subset of the population that is being studied

Question 3

Parameter

Answer 3

Any value we obtain that is characteristic of the population

E.g. the average income of Australian office workers

Question 4

Descriptive statistics

Answer 4

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

Question 5

Inferential statistics

Answer 5

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

Question 6

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

Answer 6

Sampling error

Question 7

Variable

Answer 7

Something that can take on different values

E.g. Age, speed, time

Question 8

A variable that has a limited number of values

E.g. Gender, set categories

Answer 8

Discrete variable

Question 9

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

Answer 9

Continuous variable

Question 10

Dependant variable

Answer 10

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

Question 11

Independant variable

Answer 11

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

Question 12

Measurement data

Answer 12

Generally the mean, variance, and standard deviation

E.g. Mean age of students

Question 13

Categorical data

Answer 13

Generally percentages and frequencies

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

Question 14

Nominal measurement scale

Answer 14

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

Question 15

Ordinal measurement scale

Answer 15

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)

Question 16

Interval measurement scale

Answer 16

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

Question 17

Ratio measurement scale

Answer 17

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

Question 18

What are the different kinds of measurement scales?

Answer 18

Nominal
Ordinal
Interval
Ratio

Question 19

Frequency distribution

Answer 19

How often each score appears on in a dataset.

Can be difficult to determine trends in larger datasets.

Question 20

Same info as a frequency distribution, but graphically illustrated.

Answer 20

Histogram

Question 21

Stem and leaf plots

Answer 21

Can summarise data in a simple way

Question 22

Normal distribution

Answer 22

Most scores in the middle, fewer in the extremes

Question 23

Bi-model distribution

Answer 23

When a frequency distribution has two peaks

Question 24

Positive skew

Answer 24

Most scores at the low end of the scale

Question 25

Negative skew

Answer 25

Most scores at the high end of the scale

Question 26

Kurtosis

Answer 26

Refers to how flat or peaked the distribution appears

Question 27

Leptokurtic

Answer 27

Distribution characterised by high peak at the centre of the scale

Question 28

Platykurtic

Answer 28

Distribution is flatter, with less scores in the centre

Question 29

Central tendency

Answer 29

The tendency of a random variable to cluster around is mean, median, or mode

Question 30

Variability

Answer 30

How good is the mean as a representation of the data?

Question 31

Low variability

Answer 31

The mean is a good representation of the data

Question 32

High variability

Answer 32

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

Question 33

Average deviation

Answer 33

1. Calculate the mean
2. Calculate how much each score deviates from the mean
3. Calculate average of the deviation

Question 34

Absolute deviations

Answer 34

When only absolute values are used (remove the negative factor)

Question 35

Variance
Represented by?
Useful for?
Equation?

Answer 35

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

Question 36

Standard deviation
Equation?
Correlation with variance?

Answer 36

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

Question 37

Mean = 0
σ = 1

Answer 37

Standard normal distribution

Question 38

Z-scores

Equation?

Answer 38

Indicates how far from the mean a data point is

z = (x - x̅) / σ

Question 39

μ

Answer 39

mean of population

Question 40

σ

Answer 40

Standard deviation

‘s’ commonly used in lieu

Question 41

x̅

Answer 41

mean

Question 42

Setting probable limits on z
Definition?
Use?
Equation?

Answer 42

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

Question 43

Sampling error

Answer 43

Difference in means between population and sample

Question 44

Hypothesis testing

Answer 44

Being able to test our hypothesis to determine whether we discount chance errors in the result or if there is a meaningful result

Question 45

Sampling distributions

Answer 45

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.

Question 46

Standard error

Answer 46

Average distance between a sample mean and a population mean

Mean difference
/
Difference expected by chance (standard error)

Question 47

Hypothesis

Answer 47

Specific, testable predictions

Question 48

Null hypothesis

Answer 48

The hypothesis that there is no difference between certain characteristics in a population.
The starting point for any statistical test.

Question 49

Type I error

Answer 49

When we erroneously reject a true null hypothesis

I.e. When we say something is significant, but it isn’t

Question 50

Type II error

Answer 50

When we fail to a show that a statistic is significant when it really is

Question 51

One-tailed test

Answer 51

One directionality displayed in a test

Question 52

Two-tailed test

Answer 52

No directionality specific test

Question 53

Directionality

Answer 53

Predicting the direction of difference

E.g. We predict that the mean will be higher than the population