1: INTRODUCTION

Question 1

categorical scales of measurement

Answer 1

nominal:
- numbers or names serve as labels
- but no numberical relationship between values

Question 2

discrete or continuous scales of measurement

Answer 2

ordinal: e.g., race position
- data is organised by rank
- values represent true numerical relationships
- but intervals between values may not be equal

interval: e.g., shoe size
- true numerical relationships and intervals between values are equal
- but scale has no true 0 point

ratio: e.g. distance
- true numberical relationships, equal intervals and true zero point

Question 3

when do we use the mean to measure central tendency?

Answer 3

discrete or continous data which is normally distributed

measure of spread - standard deviation

Question 4

when do we use median to measure central tendency?

Answer 4

discrete or continuous data which is not normally distributed

measure of spread - range

Question 5

when do we use mode to measure central tendency?

Answer 5

categorical data

Question 6

when can we make claims about causality?

Answer 6

only if we have controlled for confounding variables

• using random allocation, counterbalancing etc.
• not always possible (quasi experimental designs)

Question 7

True-experimental IVs

Answer 7

• IVs are actively manipulated
• random allocation is possible
• e.g., sport context (2 levels: solo, competitive)
• e.g., treatment group (3 levels: placebo, drug, counselling)

Question 8

Quasi-experimental IVs

Answer 8

• IV reflects fixed characteristics
• random allocation is not possible (must be cautious about implying causality)
• e.g. handedness (2L: right, left)
• e.g. age (3L: 18-20yr, 20-22yr, 22-24yr)

Question 9

between-subjects design

Answer 9

independent groups
- participants exposed to only one IV level
- e.g. intervention vs. control

Question 10

within-subjects design

Answer 10

repeated measures
- participants exposed to all IV levels

Question 11

mixed designs

Answer 11

at least one IV is between subjects AND at least one IV is within subjects

Question 12

kurtosis

Answer 12

the sharpness of hte peak of a frequency distribution curve

Sharpest to least sharp:
- leptokurtic: small sd
- mesokurtic
- platykurtic: large sd

Question 13

skew

Answer 13

positive skew - to the left (y axis)

negative skew - to the right (away from y axis)

Question 14

bimodal distribution

Answer 14

bell shaped
BUT
2 peaks

not normally distributed (don’t use parametric)

Question 15

uniform distribution

Answer 15

all values are the same (appears like a block)

not normally distributed (don’t use parametric)

Question 16

population and sample parameters

Answer 16

PP: the true value
μ = …, 𝜎 = …

SP: the estimate
x (line on top), s = 8.19

Question 17

sample error

Answer 17

degree to which sample statistics differ from underlying population parameters

minimising error:
- representative (random selection)
- sufficient in size

Question 18

Z-scores

Answer 18

converted from a normally distributed population
Z= (x-𝜇) / 𝜎

95% of values lie within +- 1.96 standard deviations of the mean

Question 19

sampling distribution

Answer 19

distribution of a statistic across an infinite number of samples (e.g., sampling distribution of the mean)

if the mean of each sample is plotted, infinite samples will form a normal distribution
- the mean of the sampling distribution of the mean is equivalent to the population mean
- the standard deviation of the sampling distribution of the mean is called - the standard error

Question 20

standard error

Answer 20

• the standard deviation of the sampling distribution
• a function of sample size
• SE decreases as sample size increases (sampling error decreases as sample size increases)

SE = 𝜎 / -/n
(standard deviation / square root n)

Question 21

estimated standard error

Answer 21

• sampling distributions are theoretical, we never know real SE
• ESE is an estimate of standard error, based on our sample

ESE = s/ -/n

Question 22

confidence intervals (CIs)

Answer 22

we use statistics to estimate population parameters
- x (line over) is a single point estimate of 𝜎
- estimates are subject to sampling error

CIs are interval estimates of population parameters (usually 95%)

We are declaring that there is still a chance that our estimates are wrong

Question 23

finding the population mean (CIs)

Answer 23

• if there’s a 95% chance that the sample mean falls within the 95% bounds of the population mean it follows that:
• theres a 95% chance that the population mean falls within the 95% CIs of the sample mean

Question 24

calculating confidence intervals

Answer 24

t-distribution
- spread of scores vary accoring to sample size

to calculate 95% CIs
- look for critical value of t where 2.5% of scored are higher/lower (t^0.975)
- 95% CIs around X(line):
x(line) +_ t^0.975 * ESE

NB. where n>1000, t^0.975) = 1.96

Question 25

null hypothesis (H^0)

Answer 25

there is no difference between the population means
- start by assuming this is true

if we find a difference between the sample means, we ask:
- what is the chance of measuring a difference of that magnitude if the null hypothesis is true

Question 26

P-values

Answer 26

p-value: the probability of measuring a difference of that magnitude if the null hypothesis is true

a (alpha) : threshold level of probability where we will be willing to reject the null hypothesis
- typically a = .05

if P < a (or equal) we reject the null hypothesis

Question 27

type I error

Answer 27

the null hypothesis is true
we reject the null

Question 28

type II error

Answer 28

the null hypothesis is false
we fail to reject the null