MCQs
11212 questions found
A.
Independent samples t-test.
✓
C.
Independent samples t-test.
✓
A.
It makes it easier to recruit participants.
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B.
It often necessitates a very large sample size to detect a meaningful effect, which can be challenging to achieve.
✓
C.
Sample size is irrelevant for rare diseases.
✓
D.
It means the drug will always be effective.
✓
A.
Simple linear regression.
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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.
✓
D.
Interquartile range.
✓
A.
The new drug is definitely better.
✓
B.
There is no statistically significant difference between the two treatments.
✓
C.
The standard treatment is better.
✓
D.
The study is invalid.
✓
A.
Independent samples t-test.
✓
C.
Chi-square test of independence.
✓
A.
Binomial distribution.
✓
B.
Poisson distribution.
✓
C.
Normal distribution, regardless of the population distribution.
✓
D.
Uniform distribution.
✓
B.
Independent samples t-test.
✓
C.
Pearson correlation.
✓
