Week 2: Probability, Odds, OR, RR, RD, Chi-square, Central limit Theorem, Sampling Distribution, CI, St Error

Question 1

What is the difference between Probability and Odds?

Answer 1

Not the same!
Prob = 1 out of 5
Odds = prob of picking/prob of not picking=1 to 4

Question 2

What measure of association is suitable to express the strength of the relation between one binary variable and one continuous variable

Answer 2

Pearson correlation

Question 3

What measure of association is suitable to express the strength of the relation between two continuous variables?

Answer 3

OR, Pearson

Question 4

What measure of association is most suitable to express the strength of the relation between two binary variables?

Answer 4

Pearson

Question 5

For which design is the OR appropariate for 2 binary vaiables ?

Answer 5

a. A cohort study
b. A case-control study
c. An experiment with two conditions and a binary outcome

Question 6

For which dsign is RR appropariate?

Answer 6

a. A cohort study
c. An experiment with two conditions and a binary outcome

Except CASE CONTROL, BECAUSE It is not meaningful to calculate the relative risk in a case-control study, because the ‘risk’ is defined by the researchers.

Question 7

What frequencies are compared with the observed frequencies when the chi-square test statistics is obtained?

Answer 7

The frequencies that are expected if there is no relation between 2 variables

Question 8

The chi-square test statistic for a contingency table can be calculated to examine the relation between

Answer 8

2 catego variables

Question 9

Yes No
Restricted diet 15 89
Ad libitum diet 47 37

How would we calculate the Prob? The Risk for a r.d/yes?
The Odds r.d/yes?
The OR?
The RR?

Answer 9

The risk: 15/104
The odds: 15/89
OR: 1st odd/2nd odd
RR: 1st risk/2nd risk
Prob: 62/126 (sum ofyes/sum of no)

Question 10

Yes No
Restricted diet 15 89
Ad libitum diet 47 37

Assume that there is no relation

Answer 10

15+89=104
47+37=84
104+84=188
104/188=
84/188=

Question 11

What is the chi-square?

How is it calculated?

Answer 11

it tells us whether two variables are independent of one another.
x,y can have 2 or more levels but the groups formed by x are independent
if x is exposure, the exposed and non-exposed groups should be independent of one another

Question 12

Chi-square relies on:

Answer 12

1. having a large sample size
2. it uses a theoretical probability distribution (Chi square distirbution)
3. it assumes a large sample and a theoretical probability distribution

Question 13

What is the Central Limit Theorem?

Answer 13

if the individuals sampled from the population are independent and if take a large sample size or if the distribution of the individuals is approx normal=> then the sampling distribution will be approx normal

Question 14

When is the sampling distribution exactly normal?

Answer 14

When it is centered, symmetrical and unimodal.

When n→ ∞, because the bigger the n, the more the sampling distribution approaches a normal distribution.

Question 15

The standard error (SE) of the average estimate depends on the sample size. What is the SE if the sample size is equal to n=100? What is the SE if the sample size is equal to n=10000?

Answer 15

SE (100)=s/√n=10/√100=3.16

SE (10000)=s/√n=10/√10000=0.1

Question 16

Why should the standard error of the average estimate for n=100 be larger than for n=10000 (note: it is assumed σ is known and fixed in both samples)?

Answer 16

The standard error gets bigger for smaller sample sizes because standard error tells you how close your estimator is to the population parameter. So a sample of a bigger size, logically, would produce a closer estimate.

Question 17

How does the sampling distribution look when the sample is equal to the whole population?

Answer 17

The mean will be exactly the same all the time. No variability. The value of the standard error is 0. The sampling distribution will look like ‘1 bar’ at the mean.

Question 18

How does the sample distribution look like when the sample is equal to the whole population?

Answer 18

The sample distribution would look like the population distribution

Question 19

How does the sampling distribution look like when the sample size is equal n=1?

Answer 19

The same as the observation mean. The distribution will have 1 baruniform distribution.

Question 20

What is the difference between

• Sample Distribution
• Sampling Distribution
• Population Distribuion