inferential statistics

Question 1

inferential statistics

Answer 1

• Descriptive statistics describe a sample

* Inferential statistics allow us to make inferences about the larger population

Question 2

Determining Analyses

Answer 2

• Need to know method
• Analysis will depend on study design
• Once we know this, we can work out what analysis to use

Question 3

Univariate data

Answer 3

• So far we’ve looked at one variable (univariate data)
• Summarising
• Visualising
• Understanding
• Univariate data can only really answer simple questions

Question 4

bivariate data

Answer 4

• Univariate means 1 variable
• Bivariate means 2 variables
• Bivariate data can be:
• 2 continuous variables
• 2 categorical variables
• 1 continuous and 1 categorical variable

Question 5

building a model

Answer 5

• Lots of different variables influence RPE
• Use a path diagram to model relationship
• We hypothesise that there will be a relationship between caffeine and RPE
• Univariate data can only really answer simple questions
• We usually want to answer more interesting questions
• Bivariate data allow us to do this

Question 6

scatterplpots

Answer 6

• Numbers often aren’t easy to understand
• Visualising data gives us a head-start
• Statistics don’t give us the full picture
• Scatterplots help us to understand our data
• Used with 2 continuous variables
• One on each axis
• Doesn’t matter which order
• We can look at:
• The pattern of the data
• The spread of the data
• The orientation of the data

Question 7

interpreting scatterplots

Answer 7

• Scatterplots help to describe our sample
• Any relationships / patterns between variables
• How much of a relationship
• The type of relationship
• Outliers in the data

Question 8

linear relationships

Answer 8

• Linear = straight line
• As one variable changes, the other variable changes
• e.g. RPE increases as caffeine increases
• The rate of change remains constant
• e.g. a 5mg increase in caffeine results in 2.5 increase in RPE

Question 9

curvilinear relationships

Answer 9

• Most common example is Yerkess-Dodson
• As one variable changes another variable changes, but only up to a certain point
• After that point, there’s either no relationship, or the direction of the relationship changes

Question 10

exponential relationships

Answer 10

• As one variable changes another variable changes
• Unlike linear relationships, the other variable changes exponentially
• i.e. as x increases, the rate at which y changes also increases

Question 11

What do we mean by ‘change’

Answer 11

• Could mean as one variable increases, the other variable increases
• This is a positive relationship
• Could mean as one variable increases, the other variable decreases
• This is a negative relationship

Question 12

no relationship

Answer 12

• Sometimes there is no relationship
• Scatterplot points don’t form a pattern
• Linear, curvilinear, or exponential
• Negative or positive
• There is no meaningful relationship between two variables