When sample is at least 20% of the population?
When it is used even though the sample is not SRS.
When confidence level or the P-value does not change very much even when the conditions are not fully met.
When the correlation between the test statistic and the P-value is close to 1.0 (or the correlation between the level of confidence and z* ic close to 1.).
The mean free-throw score for Teachers equals the mean for Deacons.
The mean free-throw score for Teachers is significantly less than the mean for Deacons.
The mean free-throw score for Deacons is significantly less than the mean for Teachers.
The mean free-throw score for Deacons is not significantly less than the mean for Teachers.
X ̅ ± z*σ / √n
X ̅ ± t*σ ⁄ √n
x ̅ ± z*s ⁄ √n
X ̅± t*s ⁄ √n
x ̅ ± z* σ ⁄ √n
X ̅ ± z* s ⁄ √n
x ̅ ± t* σ ⁄ √n
x ̅ ± t* s ⁄ √n
No because the mean daily items sold cannot be negative.
No, because the interval tells us the mean daily items sold for the two game consoles and doesn’t provide information for comparing them.
No, because the confidence interval contains zero.
Yes, because the confidence interval contains zero.
Yes, because we are 95% confident that the difference between the mean daily items sold for PS3 and WII is somewhere between -20 and 10.
No because the mean daily items sold cannot be negative.
No, because the interval tells us the mean daily items sold for the two game consoles and doesn’t provide information for comparing them.
No, because the confidence interval contains zero.
Yes, because the confidence interval does not contains zero.
Yes, because we are 95% confident that the difference between the mean daily items sold for PS3 and WII is somewhere between -20 and 10.
A
B
C
D
A
B
C
D
A
B
C
D
A
B
C
D
A
B
C
D
A
B
C
D
A one sample t-test for means (not matched pairs).
A two sample t-test for means.
A matched pairs t-test for means.
Analysis of Variance (ANOVA)
A one sample t confidence interval estimate
A one sample t-test for means (not matched pairs).
A two sample t-test for means.
A matched pairs t-test for means.
Analysis of Variance (ANOVA)
A one sample t confidence interval estimate
A one sample t-test for means (not matched pairs).
A two sample t-test for means.
A matched pairs t-test for means.
Analysis of Variance (ANOVA)
A one sample t confidence interval estimate
A one sample t-test for means (not matched pairs).
A two sample t-test for means.
A matched pairs t-test for means.
Analysis of Variance (ANOVA)
A one sample t confidence interval estimate
There is no significance difference between the mean count of good cells of the four groups.
The mean count of good cells is significantly different for all four groups.
The mean count of good cells of the high dosage group is significantly greater than the mean count of good cells of the control and low dosage.
On the basis of the P-value, the mean of at least one group differs significantly from the others, but there is no information in the ANOVA outout to determine which mean differs.
The P-value for the F test statistic is less than α=0.05.
The largest standard deviation divided by the smallest standard deviation is less than 2.
The pooled standard deviation equals 0.4331 which is greater than α=0.05.
There is no information given in the ANOVA output to determine whether the variances are equal.
α is set very low.
The sample standard deviation is not large.
There are no outliers nor strong skewness in the data.
The data are paired.
The probability that the mean number of months from returning to getting married by all RM’s was 112 months is 0.04.
The probability that the mean number of months from returning to getting married by all RM’s does not exceed 12 months is 0.04.
Only 4% of the RM’s who get married after 12 months or less after their mission; the 96% of the remaining RM’s get married after more than 12 months of returning from missions.
If the average number of months RM’s get married after their mission was indeed 12 months, the probability that RM’s get married after 16 months or greater is .04.
0.0136
0.1876
0.5164
1.8754
4.5164
No, because a 15-minute difference is probably too small to matter.
No, because the sample is not large enough.
Yes, because the P-value is less than α
Yes, because the observed mean is less than 10.
Yes, because results that are statistically significant are also practically significant.
No, because 3.1 is within one standard deviation of the sample mean
No, because the confidence interval does not include the value 3.1.
Yes, because the BYU students are smarter.
Yes, because the sample mean of 2.95 is less than 3.1.
Yes, because the confidence interval does not include the value 3.1.
Decide to purchase the plasma tv when the mean life in hours really is 30,000 hours.
Decide to purchase the plasma tv when the mean life in hours reall is less than 30,000 hours.
Decide NOT to purchase the plasma tv when the mean life in hours really is 30,000 hours.
Decide NOT to purchase the plasma tv when the mean life in hours really is less than 30,000 hours.
We did not make an error because the P-value is small
We made a type II error, but not a type I error
We made a type I error, but not a type II error
We made both a type I and a type II error
We did not make an error because the P-value is small
We made a type II error, but not a type I error
We made a type I error, but not a type II error
We made both a type I and a type II error
The same as in BYU-Provo, because the two sample sizes are the same.
Smaller than in BYU-Provo, because the population is much larger in BYU-Provo than BYU-Salt Lake Center.
Larger than BYU-Provo, because there are fewer students in BYU-Salt Lake
May be either smaller or larger than BYU-Provo.
Take a small sample of shirts.
Take a large sample of shirts.
Wear each one of them.
Let the other team wear them.
The range of reasonable values for the true mean GPA of students enrolled at the BYU Salt Lake Center.
The range of reasonable values for the sample mean GPA of students enrolled at the BYU Salt Lake Center.
The range of reasonable values for the true standard deviation of GPA of students enrolled at the BYU Salt Lake Center.
The range for 90% of GPA’s of all students enrolled at the BYU Salt Lake Center.
The researcher is biased against minorities.
The researcher does not like Sotomayor.
Multiple analyses increase the chances of making a Type I error.
Multiple analyses increase the chances of making a Type II error.
Take a random sample of 100 mean, ask them who smoke and who does not. Record the incidence of lung cancer between those who smoke and those who do not.
Ask for volunteer of 100 mean, ask them who smoke and who does not. Record the incidence of lung cancer between those who smoke and those who do not.
Take a random sample of 100 men. Randomly assign them into two groups. Ask the first group to smoke for 10 years and the other group not to smoke. After 10 years measure the incidence of lung cancer between the two groups using a two sample t-test.
Randomly ask 100 lawyers whether smoking causes cancer.
Growing rats
Height of jump
Bone density as measured in mg/cm3.
Age of rat
No, because the p-value is not less than alpha
No, because the results are statistical significant.
No, because the p-value is less than alpha.
Yes, because the p-value is not less than alpha.
Yes, because the p-value is less than alpha.
No, because the p-value is not less than alpha
No, because the results are statistical significant.
No, because the p-value is less than alpha.
Yes, because the p-value is not less than alpha.
Yes, because the p-value is less than alpha.
An outcome that is quite likely to happen if Ho were true is good evidence that Ho is true.
If the probability of Ho being true is very small, then Ho cannot be true.
If the probability of an outcome occurring is quite likely, then the claim as stated in Ho about the outcome must be correct.
An outcome that would rarely happen if Ho were true is good evidence that Ho is not true.
Values of the measurements in the sample data set.
All possible measurements for the response variable and their frequencies.
The population of measurements about which we wish to make an inference.
All possible values of x-bars and how often they occur if Ho were true.
If this study were to be repeated a large number of times, approximately 95% of the confidence interval would include the true mean.
95% of the students’ GPA in the population are contained in the confidence interval.
The interval contains 95% of the sample GPAs.
If this study were to be repeated a large number of times, 95% of the sample means would be included in this particular confidence interval: (2.35, 3.87).
The data are consistent with the null hypothesis. Therefore, we do not reject the null hypothesis.
The data are consistent with the null hypothesis. Therefore, we reject the null hypothesis.
The data are not consistent with the null hypothesis. Therefore, we do not reject the null hypothesis.
The data are not consistent with the null hypothesis. Therefore, we reject the null hypothesis.
There is a 2% chance that the null hypothesis is true. Therefore, we reject the null hypothesis.
The data are consistent with the null hypothesis. Therefore, we do not reject the null hypothesis.
The data are consistent with the null hypothesis. Therefore, we reject the null hypothesis.
The data are not consistent with the null hypothesis. Therefore, we reject the null hypothesis.
There is a 2% chance that the null hypothesis is true. Therefore, we reject the null hypothesis.
There is a 2% chance that the alternative hypothesis is true. Therefore, we accept the null hypothesis.
90% of the GPA of students are between (2.35, 3.87).
90% of the time, the mean GPA of students will be in the interval from 2.35 to 3.87.
We are 90% confident that the mean GPA of students is between 2.35 and 3.87.
We are 90% confident that the interval (2.35, 3.87) contains the mean GPA of this sample of 50 students.
No other checks necessary.
Whether the plot of the population is approximately Normal.
No pattern in the residual plot.
No outliers or strong skewness in a plot of the data.
Using a confidence level of 99%
Using a sample of 50 students.
Using a sample of 100 students.
Taking a different sample of 64 students.
Using a confidence level of 99%.
Using a confidence level of 90%
Using a sample of 100 students
Taking a different sample of 64 students.
Ho: µ = 3.2 and Ha: µ > 3.2
Ho: µ = 3.2 and Ha: µ < 3.2
Ho: µ = 3.2 and Ha: µ ≠ 3.2
Ho: x ̅ =3.2 and Ho: x ̅ >3.2
Ho: µ = 3.2 and Ha: µ > 3.2
Ho: µ = 3.2 and Ha: µ < 3.2
Ho: µ = 3.2 and Ha: µ ≠ 3.2
Ho: x ̅ =3.2 and Ho: x ̅ ≠ 3.2
Is the sample or experiment properly designed?
Is the observed effect too large to be due to chance alone?
How much confidence can be placed in the observed effect?
What is the probability that the parameter is different from the statistic?
170
170.7
171
100
It would get smaller.
It would stay the same.
It would get bigger.
It would get smaller.
It would stay the same.
It would get bigger.
A
B
C
D
A
B
C
D
A
B
C
D
The probability that the null hypothesis is true.
The probability that the alternative hypothesis is wrong.
The probability of rejecting the null hypothesis when in fact the null hypothesis is true.
The probability that the alternative hypothesis is false, assuming the null hypothesis is true.
. the probability of obtaining a test statistic that has a value at least as extreme as that actually observed, assuming the null hypothesis is true.
0.0830
0.0228
0.05 < p-value <0.10
0.10 < p-value < 0.20
0.0830
0.0228
0.05 < p-value <0.10
0.10 < p-value < 0.20
Yes, because an SRS of 25 players was selected.
Yes, because an SRS was taken and we can assume that free-throw percentages is Normally distributed.
No, because the t distribution is not robust in this case since there is an outlier.
No, because no standard deviation is given.
T
F
T
F
T
F
T
F
T
F
T
F
T
F
T
F
T
F
T
F
T
F
T
F
T
F
T
F