stats

Question 1

null hypothesis

Answer 1

based on normal conditions eg 0.5 probability of heads when tossing a fair coin

Question 2

alternative hypothesis

Answer 2

when the probability is not “normal”

Question 3

significance level

Answer 3

you set a lower tail and upper tail with anything above or below these numbers allowing you to reject the null hypothesis

Question 4

combination

Answer 4

doesn’t care about the order of things for example 3 cards in order A,b,c is the same as 3 cards in order b,c,a

Question 5

permeantation

Answer 5

does care about order eg number for a safe so different orders of the same numbers are different possible answers

Question 6

If you multiply all your data points by a number what happens to mean and sd

Answer 6

mean and sd are both multiplied by that number

Question 7

if you add a number to all data points what happens to mean and sd

Answer 7

add number to mean, nothing happens to sd

Question 8

sum of squared deviation (sxx)

Answer 8

sum of (x-mean)^2

Question 9

sd

Answer 9

1. root(sxx/n-1)

2. root(sum of the x^2-n x mean^2/n-1)

Question 10

lower boundary outliers

Answer 10

1. LQ-1.5 x IQR

2. mean - 2 x sd

Question 11

upper boundary outliers

Answer 11

1. UQ+1.5 x IQR

2. mean + 2 x sd

Question 12

how do you know if 2 events are mutually exclusive

Answer 12

p(A u B)= p(A)+P(B)

p(A n B)=0

Question 13

p(B|A)

Answer 13

p(B|A)=P(BnA)/P(A)

Question 14

P(B|A’)

Answer 14

P(B|A’)=P(BnA’)/p(A’)

Question 15

parent population

Answer 15

set of all possible data points from which you will draw your sample

Question 16

census

Answer 16

When population is small enough data can be collected from every member of the population

Question 17

Sampling fraction

Answer 17

Sample size/population size

Question 18

Sampling error

Answer 18

The difference between an estimate derived through stats and its true value

Question 19

Simple random sampling

Answer 19

assign everyone in the population are number and generate random numbers (shift, decimal point, x by total in pop.). Take information from those with corresponding numbers. You should take no less than 30 samples

Question 20

pros and cons of simple random sampling

Answer 20

• equal chance of getting chosen so it will provide an accurate picture of the population and a spread
• however it’s time consuming and access to the entire population is unlikely

Question 21

Stratified sampling

Answer 21

The population is divided into different groups which will have different information (for example tomatoes sizes with a population divided into tomato varieties). Each group should be represented in your sample asa percentage of the sample it takes up equal to the percentage of the population the group takes up

Question 22

Pros and cons of stratified sampling

Answer 22

• Results are likely to accurately reflect the population studied and take into account a wide spread
• you can’t always divide the population into groups and sometimes members will not fit into any group or will fit into multiple

Question 23

Cluster sampling

Answer 23

The population is in groups but there is no reason to suspect the information between groups will be hugely different so use one or more groups as the sample

Question 24

Pros and cons of cluster sampling

Answer 24

• very easy to conduct

- clusters are likely to have been picked by human bias so limited in how representative they are

Question 25

Systematic sampling

Answer 25

Take a sample at regular intervals for example every 10th person

Question 26

Pros and cons of systematic sampling

Answer 26

• it’s very simple and when the population is large you’re likely to get a widespread
• makes data manipulation easier and so bias/skewed results due to targeted outcome

Question 27

Quota sampling

Answer 27

You don’t care about groups in the population so you decide in advance how many from each group you will use

Question 28

Pros and cons of quota sampling

Answer 28

• easy and can accommodate population proportions to improve accuracy
• unsure if you can fill quota and non-random so gives researchers a lot of say

Question 29

Opportunity sampling

Answer 29

You use anyone you can get for example drop a net and use the cod you catch

Question 30

Pros and cons of opportunity sampling

Answer 30

• quick and easy

- easily manipulated by only talking certain people/cold calling in one area

Question 31

Self-selecting sample

Answer 31

Use whoever allows you to sample them

Question 32

pros and cons of self-selecting sample

Answer 32

• can allow you to get to get a spread

- tends to be a certain type of person so can make the results exaggerated and unrepresentative