Term
| 1. The change in the width of a confidence interval when the sample size is increased |
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Definition
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Term
| 2. The change in the width of a confidence interval when the level of confidence is increased |
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Definition
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Term
3. With 90% confidence, the maximum amount that the statistic differs from the parameter for the middle 90%
of all possible statistics. |
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Definition
| 3. What is margin of error. |
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Term
| 4. Its purpose is to give a range of plausible values for the population parameter. |
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Definition
| 4. What is confidence interval. |
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Term
5. The name for how often the confidence interval estimation procedure produces 98% confidence intervals that
contain the value of the parameter being estimated. |
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Definition
| 5. What is level of confidence or 98%. |
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Term
| 6. When the level of confidence is determined in a statistical problem solving procedure. |
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Definition
| 6. What is in the planning stage. |
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Term
| 7. The effect of increasing desired margin of error on the required sample size. |
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Definition
| 7. What is decreasing required sample size. |
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Term
| 8. The probability that a computed confidence interval contains the value of the parameter it estimates. |
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Definition
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Term
| 9. What we use to find out the margin of error for estimating μ with a confidence interval. |
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Definition
| 9. What is sampling distribution of x-bar. |
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Term
| 10. Fill in the blank: If x-bar is within margin of error of μ, then μ will be within _______ of x-bar. |
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Definition
| 10. What is margin of error. |
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Term
| 1. A list of the possible values of x-bar if H0 (h-naught): μ = μ0 is true. |
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Definition
| 1. What is sampling distribution of x-bar centered at μ0. |
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Term
| 2. The biggest value that P-value could be. |
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Definition
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Term
| 3. A test of significance on a small random sample that has a lot of chance variation. |
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Definition
3. What is possibly an insignificant result due to the small sample size when there should be statistical
significance. |
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Term
| 4. A test of significance on a very large random sample that has very little chance variation. |
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Definition
| 4. What is possibly a significant result due to the very large sample size when there is should not be significance. |
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Term
| 5. The probability that null hypothesis is true. |
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Definition
| 5. What is zero or one. (Note: Thinking that this is a statement for P-value is a misconception.) |
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Term
6. Appropriate statistical conclusion when using the 95% confidence interval for μ1 – μ 2, namely, using the
interval (–2.23, 1,17) to test H0: μ1 – μ2 = 0. |
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Definition
| 6. What is failing to reject the null hypothesis since zero is contained in the interval. |
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Term
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Definition
| 7. What is the parameter for comparing two population means. |
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Term
8. Procedure for analyzing data where the explanatory variable is categorical with three or more categories and
the response variable is quantitative. |
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Definition
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Term
9. Procedure for analyzing data where the explanatory variable is categorical with only two categories and the
response variable is quantitative. |
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Definition
| 9. What is a two-sample t procedure. |
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Term
10. Random allocation of individuals to treatments or random selection of individuals from independent
populations. |
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Definition
| 10. What are the two appropriate methods of data collection for valid inference. |
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Term
| 1. More than one statistical analysis performed on a data set. |
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Definition
| 1. What is multiple analyses. |
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Term
2. Results of a significant test of hypotheses where the difference is not large enough to be important or of
worth. |
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Definition
| 2. What is not practically significant. |
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Term
3. Results of a test where the data were not appropriately collected through probability sampling or
randomization. |
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Definition
| 3. What are results that may be worthless or meaningless. |
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Term
4. Using the formula x-bar plus/minus t* (times) sample (s) over the square root of n.
for data from a stratified sample. |
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Definition
| 4. What is using the wrong formula. |
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Term
| 5. Making a big deal about a P-value of 0.049 and declaring a P-value of 0.051 to be not significant. |
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Definition
| 5. What is making a distinction when there is no practical distinction. |
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Term
1. The grouping of experimental units according to some similar characteristic where the random allocation is
carried out separately within each group. |
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Definition
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Term
| 2. The condition eliminated by randomly allocating individuals to treatments. |
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Definition
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Term
| 3. Results of a study that differ too much from what we expect due to just randomization to attribute to chance. |
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Definition
| 3. What is statistically significant. |
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Term
| 4. The condition of having more than one individual in each treatment combination. |
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Definition
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Term
5. Fill in the blanks: The advantage of _______________ over _____________ is to remove variation associated
with the blocking variable from experimental error. |
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Definition
| 5. What is “randomized block experiment” over “completely randomized experiment”. |
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Term
| 1. The hypothesis that the researcher wants to prove or verify. |
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Definition
| 1. What is alternative hypothesis. |
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Term
| 2. The hypothesis of no change or no difference. |
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Definition
| 2. What is null hypothesis. |
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Term
| 3. Fill in the blank: Hypotheses are always statements about ____________. |
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Definition
| 3. What is parameters or parameter values. |
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Term
| 4. The conclusion you should make when P-value is less than α. |
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Definition
| 4. What is believe or conclude alternative hypothesis is correct. |
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Term
| 5. The conclusion you should make when P-value is greater than α. |
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Definition
| 5. What is Insufficient evidence to conclude alternative hypothesis is correct. |
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Term
| 6. The hypothesis assumed to be true when computing P-value. |
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Definition
| 6. What is null hypothesis. |
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Term
| 7. The type of alternative hypothesis specifying that the parameter is different from the null value. |
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Definition
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Term
| 8. The size of the values of the test statistic that give evidence against the null hypothesis. |
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Definition
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Term
9. Fill in the blank: An outcome that would rarely happen if a claim were true is good evidence that the claim is
________. |
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Definition
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Term
10. A probability of obtaining a value of a statistic as far or farther from the observed value if the null hypotheses
were true. |
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Definition
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Term
| 1. The largest risk a researcher is willing to take in rejecting a true null hypothesis. |
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Definition
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Term
| 2. The error made when a true null hypothesis is rejected. |
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Definition
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Term
| 3. The error made when a false null hypothesis is not rejected. |
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Definition
| 3. What is type II error. |
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Term
| 4. The probability of rejecting a true null hypothesis. |
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Definition
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Term
| 5. The probability of rejecting a false null hypothesis. |
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Definition
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Term
| 6. The criteria we use to specify α. |
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Definition
| 6. What is seriousness of type I and type II errors. |
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Term
| 7. The hypothesis that defines the sampling distribution curve under which the area of α is displayed. |
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Definition
| 7. What is H0, the null hypothesis. |
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Term
| 8. The hypothesis that defines the sampling distribution curve under which the area of β is displayed. |
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Definition
| 8. What is Ha, the alternative hypothesis. |
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Term
| 9. The change in β when α is increased. |
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Definition
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Term
| 10. The change in power when sample size is increased. |
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Definition
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Term
1. Data where two identical measurements are taken at different times (or under different conditions) on each
individual in a sample. |
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Definition
| 1. What is matched pairs. |
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Term
2. The value for μ0 in the test statistic formula
t= x-bar minus mu-naught (μ0) over sample (s) over the square root of n when performing a matched pairs t test. |
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Definition
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Term
| 3. The checks you need to make when performing a matched pairs t test. |
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Definition
3. What are data collection and either no outliers in plot of differences or number of pairs exceeds 40 (so
Central Limit Theorem can be applied.) |
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Term
| 4. What you plot to check for skewness and outliers for a matched pairs t. |
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Definition
| 4. What is plot of differences. |
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Term
| 5. What you need to compute before you can compute the mean and standard deviation for the test statistic. |
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Definition
| 5. What are the differences within each pair. |
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Term
1. The distribution we use whenever we use sample standard deviations to estimate population standard
deviations. |
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Definition
| 1. What is the t distribution. |
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Term
| 2. The parameter used when comparing the means from two populations. |
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Definition
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Term
| 3. The value you look for in a confidence interval for μ to test H0: μ = 50. |
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Definition
3. What is the value of 50. If the value of 50 is in the confidence interval, fail to reject H0. If the value of 50 is
NOT in the confidence interval, reject H0. |
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Term
| 4. The value that determines the spread of a t distribution. |
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Definition
| 4. What are degrees of freedom. |
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Term
| 5. The value of the mean of a t-distribution. |
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Definition
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Term
| 6. The estimated standard deviation of the sampling distribution of x-bar. |
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Definition
| 6. What is sample (s) over the square root of (n) |
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Term
7. Inferential procedures whose confidence levels and P-values don’t change much when conditions are not
met. |
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Definition
| 7. What are robust procedures. |
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Term
| 8. The standard error of x-bar. |
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Definition
| 8. What is sample (s) over the square root of (n) |
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Term
9. The checks you need to make when performing a one-sample t procedure—either test or confidence interval
for μ. |
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Definition
9. What are data collection (SRS) and either no outliers in plot of data or n > 40 (so Central Limit Theorem
can be applied.) |
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Term
| 10. The checks you need to make when performing a two-sample t procedure. |
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Definition
10. What are appropriate data collection (random allocation or random selection) and either no outliers in
both plots or n1 + n2 > 40 (so Central Limit Theorem can be applied.) |
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Term
| 11. H0: μ1 = μ2 or H0: μ1 – μ2 = 0 |
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Definition
| 11. What is the null hypothesis for a two-sample t test. |
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Term
| 12. The smaller of n1 – 1 and n2 – 1. |
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Definition
| 12. What are degrees of freedom for a conservative two-sample t test. |
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Term
| 13. The value you look for in a confidence interval for μ1 – μ2 in order to test H0: μ1 = μ2. |
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Definition
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Term
| The square root of (s1) squared over (n1) plus (s2) squared over (n2) |
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Definition
| 14. What is the standard error of x-bar 1 minus x-bar 2 |
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Term
| 15. When to use a two-sample t procedure instead of a matched pairs t. |
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Definition
| 15. When the two samples are independent (completely separate). |
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Term
| 1. An analysis procedure for comparing equality of three or more means. |
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Definition
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Term
| 2. H0: μ1 = μ2 = μ3 = μ4 versus Ha: not all means are equal. |
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Definition
| 2. What are the hypotheses for comparing four means in an ANOVA procedure. |
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Term
| 3. The largest standard deviation divided by the smallest standard deviation is less than 2. |
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Definition
| 3. What is the check for the equal variance condition in ANOVA. |
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Term
4. Random allocation of individuals to treatments or random selection of individuals from independent
populations. |
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Definition
| 4. What are two ways of appropriate data collection for ANOVA. |
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Term
| 5. A confidence interval for μA and a confidence interval for μB that do not overlap. |
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Definition
| 5. What are two confidence intervals giving evidence that μA and μB differ significantly. |
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