Introduction to biostatistics

Question 1

When is Spearman’s rank correlation suitable?

Answer 1

It’s suitable if

• there is a non-linear relationship
• there are outliers
• variables are on an ordinal scale (e.g. economic status: low, medium, and high)
• the sample size is small
• x or/and y are not normally distributed

Question 1

Assumptions for a chi-squared test

Answer 1

“Chi-squared test is a way to compare whether the variation in the data is due to chance or whether it is due to one of the variables we are actually testing”

• two variables are ordinal or nominal (i.e. categorical data)
• two or more independent groups
• for a 2x2 test all expected values must be at least 5

Question 2

Parameters defining normal distribution

Answer 2

mean (µ) and standard deviation (sigma)

Question 2

Assumptions when using a one-way ANOVA

Answer 2

• normally distributed data in each group
• independence of observations
• variances (SD) are equal in all groups

Question 2

What can one use for the analysis of categorical data?

Answer 2

The chi-squared test (Pearson’s chi-squared test) to test whether there is an association between two categorical variables

Question 3

Some parametric methods

Answer 3

• pearson correlation
• T-test
• ANOVA
• linear regression

Question 4

What is numerical data?

Answer 4

Numerical data is quantitative data (actual values as data) and consists of discrete and continous data

Question 5

You have numerical data of more than two groups. Which parametric and non-parametric test do you use?

Answer 5

parametric: One-way ANOVA4

non-parametric: Kruskal-Wallis

Question 6

Type 2 error

Answer 6

H0 is not rejected when it is false

“false non-significant results”

–> happen often when the sample size is too small

Question 7

What is continous data?

Answer 7

Continuous data describes data that can take any given value Example: BMI, height, weight

Question 8

You have numerical data of two unrelated groups. Which parametric and non-parametric test do you chose?

Answer 8

parametric: unpaired t-test (compares the means) non-parametric: Mann-Whitney U test

Question 9

What is categorical data?

Answer 9

Categorical data is qualitative data and consists of nominal and ordinal data

Question 10

Assumptions when using a Kruskal-Wallis test

Answer 10

• independence of observations
• can be used when your variables are not normally distributed

Question 11

Different types of correlation analysis

Answer 11

• Pearson correlation
• Spearman’s rank correlation (non-parametric)

Question 11

SPSS

Answer 11

look at the handout if you think that’s important. I don’t ;)

Question 13

What is nominal data?

Answer 13

Nominal data is data that is not specially ordered and no comparisons about better/worse can be made. Examples: marital status, gender, blood group, name, …

Question 15

How do you check normality?

Answer 15

1) Histogram, Q-Q plot, mean and meadian are close to each other (then it is normally distributed)
2) Kolmogorov-Smirnov test and Shapiro-Wilk test (cave!! p has to be higher than 0.05 in order to say that data is normally distributed!)

Question 16

Assumptions when using an unpaired t-test

Answer 16

• independence of observations
• normally distributed data in each group
• variances (SD) are equal in both groups

Question 17

Normal distribution

Answer 17

also called Guassian distribution or bell curve

Question 18

Some non-parametric methods

Answer 18

• Spearman’s Rank Correlation
• Mann-Whitney test
• Kruskal-Wallis test
• Wilcoxon test

Question 19

Alternative hypothesis

Answer 19

assumes an effect (“there is difference/association”)

Question 21

Type 1 error

Answer 21

H0 is rejected when it is true

“false significant results”

Question 22

What is discrete data?

Answer 22

Discrete data describes the number of different events and is a whole number. Example: number of pregnancies

Question 24

Assumptions when using a Mann-Whitney u test

Answer 24

• independence of observations
• can be used when your two variables are not normally distributed

Question 25

Linear regression analysis

Answer 25

to predict the value of a variable based on the value of another variable

(a lot of “mathematical” expressions; look at the handout)

Question 26

Null hypothesis

Answer 26

assumes no effect (“There is no difference/association”)

Question 27

What is ordinal data?

Answer 27

Ordinal data is data that indicates different levels and can be coded by 0-n in order to rank the data Examples: disease stage, education level

Question 28

What are the null- and the alternative hypotheses for the chi-squared test?

Answer 28

• H null: there is no difference between the observed and expected frequencies
• H1: there is a difference between the observed and the expected frequencies