# Stats I Final Pitt

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1. When is a one-sample t-test used?\

One-sample t-test
When the population variance of a variable is unknown.
2. What is a consequence of replacing the population standard deviation with a sample standard deviation?

One-sample t-test
It changes the distribution of the test statistic. It is no longer a z-distributed but t-distributed.
3. What is the relationship between df and t- and z- distribution?

One-sample t-test
When df is small, there is a large difference between the t- and z- distribution. However, when df is large (i.e. greater than 30), there is no noticeable difference.
4. How does t-critical compare to z-critical?

One-sample t-test
t-critical will always be bigger than z-critical
5. Effect size

One-sample t-test
amount of difference in SD
6. What is estimation?

One-sample t-test
Inferential process of using statistics to estimate parameters.
7. Point Estimate

One-sample t-test
• a single number is used to estimate a parameter
• -null hypothesis testing
8. Interval Estimate

One-sample t-test
a range of values is used to estimate a parameter
9. Confidence Interval

One-sample t-test
• when an interval estimate is accompanied by a specific level of confidence (probability)
• can be computed for any statistic
10. Width of Confidence Interval

One-sample t-test
• affected by confidence level and standard deviation of a statistic
• - confidence level increases, width of confidence interval increases
• -standard deviation of a statistic increases, width of confidence interval increases
11. When is an Independent-samples t-test used?

Independent-samples t-test
used to compare a mean of the DV between two groups
12. What is the df of independent-samples t-test?

Independent-samples t-test
• df = (n1 -1) + (n2 - 1)
•     = N-2
13. Pooled Variance

Independent-samples t-test
weighted average of a variances of a DV for group 1 and 2
14. Assumptions of independent-samples t-test

Indpependent-samples t-test
• Normality
• No Outliers
• Homogeneity of Variance
• Independence of Subjects
• If violated results/conclusion of t-test could be invalid
15. Assumption: Normality

Independent-samples t-test
• DV should be normally distributed within each group
• -tested using the Shapiro-Wilk
16. Assumption: No Outliers

Independent-samples t-test
• an outlier has an undue influence on a statistic
• assumption is checked by examining histogram and Q-Q plot
17. Assumption: Homogeneity of variance

Independent-samples t-test
the variances of the dependent variable are between group 1 and 2, Leven's test of homogeneity of variance will be used to test this assumption
18. Assumption: Independence of Subjects

Independent-samples t-test
• design consideration. one subject outcome is not influenced by another's outcome
• not testable
19. Mann-Whitney U

Independent-samples t-test
• Non-parametric test
• Alternative to the independent-samples t-test
• does NOT assume normality
20. Welch's t-test

Independent-samples t-test
• changes df to reflect violation of homogeniety
• more violated the smaller the df
• less powerful than t-test

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 Author: Anonymous ID: 187199 Filename: Stats I Final Pitt Updated: 2012-12-05 03:14:21 Tags: Statistics test Correlation Regression Folders: Description: Stats Final I Show Answers:

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