What is the role of effect size in power analysis?

Larger effect size increases power

What is the relationship between sample size & margin of error?

Larger sample reduces margin of error

If power is 0.9, what does that mean?

90% chance of rejecting false H0

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biostat exam 2 1.5

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Questions 90

Answers

1
Which factor does not affect hypothesis testing power?
Alpha
Sample median
Variance
Effect size
2
A 95% confidence interval means the true parameter lies within the interval 95% of the time.
FALSE
TRUE
3
Type II error (β) is the probability of failing to reject H₀ when it is false.
TRUE
FALSE
4
What is a common nonparametric alternative to ANOVA?
t-test
Kruskal-Wallis
F-test
Chi-square
5
What happens to power when sample size increases?
No effect on power
Power increases
Alpha increases
Power decreases
6
The Mann-Whitney U test is used for comparing two independent samples.
TRUE
FALSE
7
What is the role of effect size in power analysis?
Effect size is unrelated to power
Larger effect size increases power
Effect size is only used in CI
Smaller effect size increases power
8
What does a smaller standard error indicate?
Higher variability
Narrower power
Lower confidence
More precise estimate
9
How is statistical significance determined from CI?
CI excluding null value
CI width greater than 2
CI equal to effect size
CI including null value
10
Which test is used when data is non-normal?
Mann-Whitney U
t-test
ANOVA
F-test
11
What does it mean if a CI for risk ratio includes 1?
Power is high
Significant at 0.05
No significant effect
Reject H0
12
What is the probability of making a Type II error?
1 - power
β
1 - α
Alpha level
13
Increasing the confidence level results in a wider confidence interval.
FALSE
TRUE
14
What is the relationship between sample size & margin of error?
No impact on error
Larger sample increases margin of error
Larger sample reduces margin of error
Only affects Type I error
15
Which confidence level is typically used in research?
50%
95%
80%
99.90%
16
What is a point estimate?
Best single-value estimate
Exact probability of H0
A hypothesis decision
Range of possible values
17
If power is 0.9, what does that mean?
90% chance of rejecting false H0
90% chance of Type I error
90% Type II error
90% certainty in H0
18
What does a CI of (1.2, 3.8) for odds ratio suggest?
OR is significantly >1
Power is low
β is high
No significant effect
19
Type I error (α) is the probability of rejecting H₀ when it is true.
FALSE
TRUE
20
Power is calculated as 1 - β.
FALSE
TRUE
21
What does a nonparametric test analyze?
Variance ratios
Mean differences
Proportions
Ranked data
22
Which test should be used for extreme outliers?
ANOVA
Nonparametric
F-test
Parametric
23
Which term refers to likelihood of unknown parameter?
Variance
Alpha (α)
P-value
Confidence level
24
A point estimate is the best single-value estimate for a parameter.
TRUE
FALSE
25
A CI for a mean difference that includes 0 suggests statistical significance.
FALSE
TRUE
26
Nonparametric tests are appropriate when data are not normally distributed.
TRUE
FALSE
27
Why is ranking used in nonparametric tests?
Improves variance
Eliminates Type I error
Handles non-normal data
Reduces power
28
A wider confidence interval indicates more precision in the estimate.
FALSE
TRUE
29
Power increases as sample size increases.
TRUE
FALSE
30
What is the standard formula for confidence interval estimation?
Point estimate ± Z * SE
Variance ± α-level
Power * p-value
Mean ± p-value
31
Why does a lower α require a larger sample size?
To reduce Type I error
To increase p-value
To maintain power
To eliminate variability
32
A 95% confidence level means there is a 95% chance the true value is within the interval.
TRUE
FALSE
33
Sample size affects the width of a confidence interval.
FALSE
TRUE
34
What happens if a CI contains zero?
Significant at 0.05
Power is low
No significant difference
Reject H0
35
The margin of error helps determine the range in a confidence interval.
FALSE
TRUE
36
A confidence interval provides a range of plausible values for a parameter.
FALSE
TRUE
37
A point estimate is a single-value estimate of a population parameter.
FALSE
TRUE
38
What is the purpose of estimation in statistics?
Prove hypotheses
Find exact population value
Compute F-statistic
Determine likely values
39
The Sign test is used to compare paired samples.
FALSE
TRUE
40
Why use nonparametric tests?
To find proportions
To estimate variance
Data is non-normal
To increase power
41
Which nonparametric test compares two independent samples?
Wilcoxon Signed-Rank
Mann-Whitney U
Kruskal-Wallis
t-test
42
What is the key difference between parametric and nonparametric tests?
Significance calculation
Use of confidence intervals
Normality assumption
Effect size estimation
43
If a CI for an odds ratio does not include 1, the result is statistically significant.
FALSE
TRUE
44
What does not affect sample size determination?
Number of groups
Effect size
Significance of p-value
Variance of parameter
45
What does increasing sample size do to CI?
Narrows it
Increases margin of error
Widens it
No effect
46
When is Mann-Whitney U preferred over t-test?
Non-normal distribution
Large samples
Small variance
Paired samples
47
Power analysis helps determine the required sample size for a study.
FALSE
TRUE
48
If CI for a mean difference includes zero, what is implied?
Reject H0
Significant at 0.05
No significant difference
Power is high
49
What does a confidence interval provide?
P-value significance
Effect size
Exact value of parameter
Range for unknown parameter
50
A confidence interval estimate includes a point estimate and margin of error.
TRUE
FALSE
51
What does a 95% confidence level imply?
95% certainty of true mean
95% accuracy in data
5% Type I error
No variability in data
52
Wider CIs indicate higher precision in estimation.
TRUE
FALSE
53
The Kruskal-Wallis test is a nonparametric alternative to ANOVA.
TRUE
FALSE
54
A larger sample size decreases the probability of a Type I error.
TRUE
FALSE
55
What does a smaller margin of error indicate?
More variability
Higher p-value
More precise estimate
Lower power
56
The Mann-Whitney U test assumes a normal distribution.
TRUE
FALSE
57
What does a wider confidence interval imply?
Lower variability
More uncertainty
More precision
Smaller sample size
58
Why use confidence intervals instead of p-values?
Shows strength & direction of effect
Eliminates uncertainty
Always wider than p-values
More accurate than p-values
59
How is standard error (SE) related to CI?
Larger SE narrows CI
Smaller SE narrows CI
SE measures power
SE increases p-value
60
Hypothesis testing procedures depend on the type of data and number of groups compared.
FALSE
TRUE
61
Nonparametric tests assume normality of data distribution.
TRUE
FALSE
62
A larger effect size increases statistical power.
TRUE
FALSE
63
A lower confidence level results in a narrower confidence interval.
TRUE
FALSE
64
What affects CI width?
Only p-value
Sample size & variability
Only Type I error
Power level
65
Confidence intervals help assess real-world significance beyond statistical significance.
FALSE
TRUE
66
The standard error of a point estimate affects the width of the confidence interval.
TRUE
FALSE
67
A Kruskal-Wallis test requires normally distributed data.
TRUE
FALSE
68
Ranked data are used in nonparametric tests like the Mann-Whitney U test.
TRUE
FALSE
69
Which test is best for matched-pair nonparametric analysis?
t-test
ANOVA
Wilcoxon Signed-Rank
Kruskal-Wallis
70
Statistical power is the probability of correctly rejecting a false null hypothesis.
FALSE
TRUE
71
What does a confidence interval estimate?
Exact population value
Range for unknown parameter
Hypothesis decision
P-value significance
72
What happens when confidence level increases?
P-value decreases
CI narrows
Power decreases
CI widens
73
What is the first step in nonparametric analysis?
Rank the data
Find standard error
Calculate mean
Estimate variance
74
What is the purpose of sample size determination?
Increase Type II error
Minimize Type I error
Ensure statistical precision
Maximize p-value
75
Confidence intervals provide more insight than a single point estimate.
FALSE
TRUE
76
A hypothesis test follows a structured five-step process.
TRUE
FALSE
77
A confidence interval (CI) estimates a range where the true population parameter likely falls.
TRUE
FALSE
78
How does increasing α affect power?
Decreases power
No effect
Increases power
Decreases Type I error
79
Independent and paired samples require different statistical tests.
TRUE
FALSE
80
Which statement is true about power and sample size?
Larger sample increases power
Smaller sample increases power
Power = 1 - α
Power is independent of sample size
81
What is the confidence interval formula?
Mean ± F-statistic
Point estimate * p-value
Z * variance
Point estimate ± Z * SE
82
What is the key assumption of Mann-Whitney U?
Paired data
Equal variances
Independent samples
Normality
83
Increasing the confidence level results in a wider confidence interval.
TRUE
FALSE
84
Why does a wider CI indicate more uncertainty?
Reduced sample size
Lower power
More variability in data
Higher p-value
85
Why does a 99% CI differ from 95% CI?
Smaller variance
Lower power
More accurate
Wider range for more confidence
86
Which component does NOT influence statistical power?
Sample size
Alpha level
Effect size
Confidence level
87
Which statement about power is FALSE?
Lower α reduces power
Power = 1 - Type I error
Higher sample size increases power
Larger effect size increases power
88
The Sign test requires continuous data.
FALSE
TRUE
89
CIs show both the magnitude and direction of an effect.
FALSE
TRUE
90
What is the margin of error in confidence intervals?
Standard deviation
Effect size measure
Range around point estimate
P-value variance

biostat exam 2 1.5 Flashcards | StudBud

biostat exam 2 1.5

90 termsStudBud Administration1 play

Imported from external platform

Flashcards90 cardsFPractice quizPHost liveL

Questions 90

Answers

1
Which factor does not affect hypothesis testing power?
Alpha
Sample median
Variance
Effect size
2
A 95% confidence interval means the true parameter lies within the interval 95% of the time.
FALSE
TRUE
3
Type II error (β) is the probability of failing to reject H₀ when it is false.
TRUE
FALSE
4
What is a common nonparametric alternative to ANOVA?
t-test
Kruskal-Wallis
F-test
Chi-square
5
What happens to power when sample size increases?
No effect on power
Power increases
Alpha increases
Power decreases
6
The Mann-Whitney U test is used for comparing two independent samples.
TRUE
FALSE
7
What is the role of effect size in power analysis?
Effect size is unrelated to power
Larger effect size increases power
Effect size is only used in CI
Smaller effect size increases power
8
What does a smaller standard error indicate?
Higher variability
Narrower power
Lower confidence
More precise estimate
9
How is statistical significance determined from CI?
CI excluding null value
CI width greater than 2
CI equal to effect size
CI including null value
10
Which test is used when data is non-normal?
Mann-Whitney U
t-test
ANOVA
F-test
11
What does it mean if a CI for risk ratio includes 1?
Power is high
Significant at 0.05
No significant effect
Reject H0
12
What is the probability of making a Type II error?
1 - power
β
1 - α
Alpha level
13
Increasing the confidence level results in a wider confidence interval.
FALSE
TRUE
14
What is the relationship between sample size & margin of error?
No impact on error
Larger sample increases margin of error
Larger sample reduces margin of error
Only affects Type I error
15
Which confidence level is typically used in research?
50%
95%
80%
99.90%
16
What is a point estimate?
Best single-value estimate
Exact probability of H0
A hypothesis decision
Range of possible values
17
If power is 0.9, what does that mean?
90% chance of rejecting false H0
90% chance of Type I error
90% Type II error
90% certainty in H0
18
What does a CI of (1.2, 3.8) for odds ratio suggest?
OR is significantly >1
Power is low
β is high
No significant effect
19
Type I error (α) is the probability of rejecting H₀ when it is true.
FALSE
TRUE
20
Power is calculated as 1 - β.
FALSE
TRUE
21
What does a nonparametric test analyze?
Variance ratios
Mean differences
Proportions
Ranked data
22
Which test should be used for extreme outliers?
ANOVA
Nonparametric
F-test
Parametric
23
Which term refers to likelihood of unknown parameter?
Variance
Alpha (α)
P-value
Confidence level
24
A point estimate is the best single-value estimate for a parameter.
TRUE
FALSE
25
A CI for a mean difference that includes 0 suggests statistical significance.
FALSE
TRUE
26
Nonparametric tests are appropriate when data are not normally distributed.
TRUE
FALSE
27
Why is ranking used in nonparametric tests?
Improves variance
Eliminates Type I error
Handles non-normal data
Reduces power
28
A wider confidence interval indicates more precision in the estimate.
FALSE
TRUE
29
Power increases as sample size increases.
TRUE
FALSE
30
What is the standard formula for confidence interval estimation?
Point estimate ± Z * SE
Variance ± α-level
Power * p-value
Mean ± p-value
31
Why does a lower α require a larger sample size?
To reduce Type I error
To increase p-value
To maintain power
To eliminate variability
32
A 95% confidence level means there is a 95% chance the true value is within the interval.
TRUE
FALSE
33
Sample size affects the width of a confidence interval.
FALSE
TRUE
34
What happens if a CI contains zero?
Significant at 0.05
Power is low
No significant difference
Reject H0
35
The margin of error helps determine the range in a confidence interval.
FALSE
TRUE
36
A confidence interval provides a range of plausible values for a parameter.
FALSE
TRUE
37
A point estimate is a single-value estimate of a population parameter.
FALSE
TRUE
38
What is the purpose of estimation in statistics?
Prove hypotheses
Find exact population value
Compute F-statistic
Determine likely values
39
The Sign test is used to compare paired samples.
FALSE
TRUE
40
Why use nonparametric tests?
To find proportions
To estimate variance
Data is non-normal
To increase power
41
Which nonparametric test compares two independent samples?
Wilcoxon Signed-Rank
Mann-Whitney U
Kruskal-Wallis
t-test
42
What is the key difference between parametric and nonparametric tests?
Significance calculation
Use of confidence intervals
Normality assumption
Effect size estimation
43
If a CI for an odds ratio does not include 1, the result is statistically significant.
FALSE
TRUE
44
What does not affect sample size determination?
Number of groups
Effect size
Significance of p-value
Variance of parameter
45
What does increasing sample size do to CI?
Narrows it
Increases margin of error
Widens it
No effect
46
When is Mann-Whitney U preferred over t-test?
Non-normal distribution
Large samples
Small variance
Paired samples
47
Power analysis helps determine the required sample size for a study.
FALSE
TRUE
48
If CI for a mean difference includes zero, what is implied?
Reject H0
Significant at 0.05
No significant difference
Power is high
49
What does a confidence interval provide?
P-value significance
Effect size
Exact value of parameter
Range for unknown parameter
50
A confidence interval estimate includes a point estimate and margin of error.
TRUE
FALSE
51
What does a 95% confidence level imply?
95% certainty of true mean
95% accuracy in data
5% Type I error
No variability in data
52
Wider CIs indicate higher precision in estimation.
TRUE
FALSE
53
The Kruskal-Wallis test is a nonparametric alternative to ANOVA.
TRUE
FALSE
54
A larger sample size decreases the probability of a Type I error.
TRUE
FALSE
55
What does a smaller margin of error indicate?
More variability
Higher p-value
More precise estimate
Lower power
56
The Mann-Whitney U test assumes a normal distribution.
TRUE
FALSE
57
What does a wider confidence interval imply?
Lower variability
More uncertainty
More precision
Smaller sample size
58
Why use confidence intervals instead of p-values?
Shows strength & direction of effect
Eliminates uncertainty
Always wider than p-values
More accurate than p-values
59
How is standard error (SE) related to CI?
Larger SE narrows CI
Smaller SE narrows CI
SE measures power
SE increases p-value
60
Hypothesis testing procedures depend on the type of data and number of groups compared.
FALSE
TRUE
61
Nonparametric tests assume normality of data distribution.
TRUE
FALSE
62
A larger effect size increases statistical power.
TRUE
FALSE
63
A lower confidence level results in a narrower confidence interval.
TRUE
FALSE
64
What affects CI width?
Only p-value
Sample size & variability
Only Type I error
Power level
65
Confidence intervals help assess real-world significance beyond statistical significance.
FALSE
TRUE
66
The standard error of a point estimate affects the width of the confidence interval.
TRUE
FALSE
67
A Kruskal-Wallis test requires normally distributed data.
TRUE
FALSE
68
Ranked data are used in nonparametric tests like the Mann-Whitney U test.
TRUE
FALSE
69
Which test is best for matched-pair nonparametric analysis?
t-test
ANOVA
Wilcoxon Signed-Rank
Kruskal-Wallis
70
Statistical power is the probability of correctly rejecting a false null hypothesis.
FALSE
TRUE
71
What does a confidence interval estimate?
Exact population value
Range for unknown parameter
Hypothesis decision
P-value significance
72
What happens when confidence level increases?
P-value decreases
CI narrows
Power decreases
CI widens
73
What is the first step in nonparametric analysis?
Rank the data
Find standard error
Calculate mean
Estimate variance
74
What is the purpose of sample size determination?
Increase Type II error
Minimize Type I error
Ensure statistical precision
Maximize p-value
75
Confidence intervals provide more insight than a single point estimate.
FALSE
TRUE
76
A hypothesis test follows a structured five-step process.
TRUE
FALSE
77
A confidence interval (CI) estimates a range where the true population parameter likely falls.
TRUE
FALSE
78
How does increasing α affect power?
Decreases power
No effect
Increases power
Decreases Type I error
79
Independent and paired samples require different statistical tests.
TRUE
FALSE
80
Which statement is true about power and sample size?
Larger sample increases power
Smaller sample increases power
Power = 1 - α
Power is independent of sample size
81
What is the confidence interval formula?
Mean ± F-statistic
Point estimate * p-value
Z * variance
Point estimate ± Z * SE
82
What is the key assumption of Mann-Whitney U?
Paired data
Equal variances
Independent samples
Normality
83
Increasing the confidence level results in a wider confidence interval.
TRUE
FALSE
84
Why does a wider CI indicate more uncertainty?
Reduced sample size
Lower power
More variability in data
Higher p-value
85
Why does a 99% CI differ from 95% CI?
Smaller variance
Lower power
More accurate
Wider range for more confidence
86
Which component does NOT influence statistical power?
Sample size
Alpha level
Effect size
Confidence level
87
Which statement about power is FALSE?
Lower α reduces power
Power = 1 - Type I error
Higher sample size increases power
Larger effect size increases power
88
The Sign test requires continuous data.
FALSE
TRUE
89
CIs show both the magnitude and direction of an effect.
FALSE
TRUE
90
What is the margin of error in confidence intervals?
Standard deviation
Effect size measure
Range around point estimate
P-value variance