Stats I Final Pitt

  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
Card Set
Stats I Final Pitt
Stats Final I