# Stats I Final Pitt

 .remove_background_ad { border: 1px solid #555555; padding: .75em; margin: .75em; background-color: #e7e7e7; } .rmbg_image { max-height: 80px; } When is a one-sample t-test used?\ One-sample t-test When the population variance of a variable is unknown. 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. 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. How does t-critical compare to z-critical?   One-sample t-test t-critical will always be bigger than z-critical Effect size  One-sample t-test amount of difference in SD What is estimation? One-sample t-test Inferential process of using statistics to estimate parameters. Point Estimate One-sample t-test a single number is used to estimate a parameter-null hypothesis testing Interval Estimate One-sample t-test a range of values is used to estimate a parameter 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  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 When is an Independent-samples t-test used?  Independent-samples t-test used to compare a mean of the DV between two groups What is the df of independent-samples t-test? Independent-samples t-test df = (n1 -1) + (n2 - 1)    = N-2 Pooled Variance Independent-samples t-test weighted average of a variances of a DV for group 1 and 2 Assumptions of independent-samples t-test Indpependent-samples t-test NormalityNo OutliersHomogeneity of VarianceIndependence of SubjectsIf violated results/conclusion of t-test could be invalid Assumption: Normality Independent-samples t-test DV should be normally distributed within each group-tested using the Shapiro-Wilk Assumption: No Outliers Independent-samples t-test an outlier has an undue influence on a statisticassumption is checked by examining histogram and Q-Q plot 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 Assumption: Independence of Subjects Independent-samples t-test design consideration. one subject outcome is not influenced by another's outcomenot testable Mann-Whitney U Independent-samples t-test Non-parametric testAlternative to the independent-samples t-test does NOT assume normality  Welch's t-test Independent-samples t-test changes df to reflect violation of homogenietymore violated the smaller the dfless powerful than t-test .remove_background_ad { border: 1px solid #555555; padding: .75em; margin: .75em; background-color: #e7e7e7; } .rmbg_image { max-height: 80px; } AuthorAnonymous ID187199 Card SetStats I Final Pitt DescriptionStats Final I Updated2012-12-05T03:14:21Z Show Answers