Biostatistic Heroes
Biostatistic Heroes Quiz
Test your knowledge on statistical distributions, hypothesis testing, and bioinformatics concepts in our engaging Biostatistic Heroes Quiz!
Whether you're a seasoned biostatistician or just starting out, this quiz is designed to challenge and enhance your understanding of:
- Statistical Methods
- Data Analysis
- Biostatistics Principles
- Machine Learning Concepts
On a score from 1 to 5 [where 1 is a Newbie and 5 a full-fledged Bioinformatician], how do yo rate your knowledge on Statistical distributions?
1
2
3
4
5
Which of the following is categorical data?
[1,10]
Arkansas, Arizona, Alaksa
ISUP Grade for Prostate Cancer
Which of the following is true?
The mode is the most frequent observation in a sample
The median is the 50th percentile
The variance in a sample is the sum of deviations from the mean value
Which of the following about linear regression is true?
If I run linear regression with R, I can blindly believe the result statistics
The regression line minimises the sum of quadratic deviations of all data points to the line
Linear regression can be performed with more than one independent variable
I want to show that gene A is over expressed in cell a compared to cell b. What's a correct hypothesis pair for a test?
The truth about the test decision:
H0 can never be rejected
H0 can be rejected
H0 can be proven
H0 can never be proven
More truths about test decisions (that's a tricky one)
The smaller the p-value the higher the potential significance
The p-value takes on values between 0 and 1
P-values represent the strength of the evidence against the null hypothesis
The p value is a measure for the size of an effect
If the p-value is larger that 0.05, H1 is false
What's true about Principal component analysis?
PCA is a non-linear dimensionality reduction
A principal component (Eigen-gene) represents correlated variaton of genes
The importance of the principal component 3 is given bei Eigenvalue 3
The PCA score reflects the similarity of a gene to a principal component
PCA picks up covariance of features (genes)
'Loading' reflects the contribution of features to the principal components
PCA is easy to understand
What is true about KNN?
KNN classification needs a labeled data set
KNN can only be performed with 2 features
It is good to use small k
K is chosen to about sort(n), if n is the number of data points
KNN can be used to construct a graph
The teacher did not make a mistake in the manual clustering demonstration
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