A.
Simple linear regression.
✓
B.
Multiple linear regression.
✓
C.
Normally distributed.
✓
A.
To immediately impute all missing values.
✓
B.
To investigate the pattern of missingness and assess its potential impact on bias and the validity of results.
✓
C.
To ignore the missing values if they are less than 5%.
✓
D.
To discard all records with missing values.
✓
A.
The p-value is too high.
✓
B.
The study had too much power.
✓
C.
Loss to follow-up can introduce bias and threaten the internal validity of the study.
✓
D.
The drug is definitely effective.
✓
A.
Independent samples t-test.
✓
C.
Chi-square test of independence.
✓
A.
Descriptive statistics.
✓
B.
Inferential statistics.
✓
A.
Simple linear regression.
✓
B.
Multiple linear regression.
✓
C.
Logistic regression.
✓
A.
Statistical significance always implies clinical significance.
✓
B.
Statistical significance does not always imply clinical significance.
✓
C.
The study had too many participants.
✓
D.
The p-value is too low.
✓
A.
The probability of making a Type I error is 20%.
✓
B.
The probability of correctly rejecting the null hypothesis when it is false is 80%.
✓
C.
The probability of making a Type II error is 80%.
✓
D.
The significance level is 0.20.
✓
