Statistical theory 1

Question 1

What does probability theory do?

Answer 1

Makes a prediction

e.g. I know the coin is fair, so the probability of heads is 50%

Question 2

What do inferential statistics do?

Answer 2

Draws a conclusion (inference) based on the data

Question 3

What is the frequentist view of probability?

Answer 3

Probability is a ‘long run frequency’

It is objective

It is in the world

• If you do something infinitely, the probability will be exact*
• e.g. infinite coin flips = 50% heads and 50% tails*

Question 4

The frequentist view of probability only applies to…

Answer 4

repeatable events

Question 5

What is the ‘law of large numbers’?

Answer 5

A large sample size will probably be more representative than a small sample size.

Question 6

What is a good target for a minimum sample size?

Answer 6

25-30 participants

Question 7

What is the Bayesian view of probability?

Answer 7

Probability is degree of belief

(i.e. what does an idealised, rational agent believe will happen?)

Probability is subjective and it is only in the belief of rational agents

It applies to anything you can believe

Question 8

What is Bayes theorem?

Answer 8

In probability and statistics: describes the probability of an event, based on prior knowledge of conditions that might be related to the event

Question 9

I think Melbourne will win the cup is what view of probability?

Answer 9

Bayesian

Question 10

1 in 3 people will get cancer is what view of probability?

Answer 10

Frequentist

Question 11

Probability distributions: the probability that everything must happen must sum to __

Answer 11

1

(e.g. if i flip a coin its 50% heads 50% tails = 1)

0 = will certainly not happen, 1 = will definitely happen

Question 12

What is a binominal distribution?

Answer 12

Used to describe count data of one of two possible events happening

(bi) = two, (nominal) = number

Can capture anything where there are two possible outcomes and there is some underlying probability of success

Question 13

What is the shape of a normal distribution?

Answer 13

Bell Curve

Question 14

How many parameters does the bell curve have?

Answer 14

2

The mean and the standard deviation

Question 15

In a normal distribution, what does μ (mu) stand for?

Answer 15

The mean

Question 16

In a normal distribution, what does σ (sigma) stand for?

Answer 16

Standard deviation

Question 17

What type of variables do normal distributions apply to?

Answer 17

Continuous variables

Question 18

What are the 4 R commands for distributions in R?

Answer 18

Question 19

Name the 4 features of binominal data

Answer 19

It is the count data with two outcomes

It is discrete

Size is the number of trials

Probability is the % weight of variables