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Lecture 14 – Thurs, Oct 23 Multiple Comparisons (Sections 6.3, 6.4). Next time: Simple linear regression (Sections 7.1-7.3)

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Presentation on theme: "Lecture 14 – Thurs, Oct 23 Multiple Comparisons (Sections 6.3, 6.4). Next time: Simple linear regression (Sections 7.1-7.3)"— Presentation transcript:

1 Lecture 14 – Thurs, Oct 23 Multiple Comparisons (Sections 6.3, 6.4). Next time: Simple linear regression (Sections 7.1-7.3)

2 Compound Uncertainty Compound uncertainty: When drawing more than one direct inference, there is an increased chance of making at least one mistake. Impact on tests: If using a conventional criteria such as a p-value of 0.05 to reject a null hypothesis, the probability of falsely rejecting a null hypothesis will be greater than 0.05 if considering multiple tests. Impact on confidence intervals: If forming multiple 95% confidence intervals, the chance that all of the confidence intervals will contain true parameter is less than 95%.

3 Simultaneous Inferences When several 95% confidence intervals are considered simultaneously, they constitute a family of confidence intervals Individual Confidence Level: Success rate of a procedure for constructing a single confidence interval. Familywise Confidence Level: Success rate of procedure for constructing a family of confidence intervals, where a “successful” usage is one in which all intervals in the family capture their parameters.

4 Individual vs. Family Confidence Levels If a family consists of k confidence intervals, each with individual confidence level 95%, the familywise confidence levels can be no larger than 95% and no smaller than 100(1-.05k)%. Actual familywise confidence levels depends on degree of dependence between intervals. If the intervals are independent, the familywise confidence level is 100(.95) k %.

5 Familywise Confidence Levels KLower Bound Confidence level if independent 385%86% 575%77% 200%36% 1000%1%

6 Multiple Comparison Procedures Multiple comparison procedures are methods of constructing individual confidence intervals so that familywise confidence level is controlled (at 95% for example). Key issue: What is the appropriate family to consider?

7 Planned vs. Unplanned Comparisons Consider one-way classification with 20 groups. Planned Comparisons: researcher is specifically interested in comparing groups 1 and 4 because comparison answers a research question directly. This is a planned comparison. In the mice diets example, the researchers had five planned comparisons. Unplanned Comparisons: researcher examines all possible pairs of groups – 190 groups. As a result, researcher finds that only groups 5 and 8 suggest actual differences. Only this pair is reported as significant.

8 Families in Planned/Unplanned Planned Comparisons: The family of confidence intervals is the family of all planned comparison confidence intervals (e.g., the family of five planned comparisons in mice diet). For small number of planned comparisons, it is usual practice to just use individual confidence intervals controlled at 95%. Unplanned Comparisons: The family of confidence intervals is the family of all possible comparisons - (k*(k-1)/2) for a k-group one-way classification. It is important to control the familywise confidence level for unplanned comps.

9 Multiple Comparison Procedures Confidence Interval: Estimate Margin of Error. Margin of Error = (Multiplier)x(Standard Error of Estimate). For multiple comparison procedures, the multipliers is greater than the usual 2. Multiple comparisons procedures –Tukey/Kramer’s “Honest Significant Differences” –Bonferroni

10 Tukey-Kramer Procedure Based on computing the distribution of the largest |t| statistic under the null hypothesis that all group means are equal. Family of confidence intervals for all group mean differences that has 95% familywise confidence level: can be found on Table A.5. For df=n-I, use closest df > n-I on chart.

11 Tukey-Kramer example For multiplecomp.JMP, n=200, I=20, so from Table A.5 using df=n-I= (chart only goes up to 120), Tukey-Kramer family of confidence intervals with 95% familywise level: Examples: Tukey-Kramer confidence interval for is and are not significantly different (in sense of statistical significance) using Tukey-Kramer since CI contains 0.

12 Tukey-Kramer in JMP To see which groups are significantly different (in sense of statistical significance), i.e., which groups have CI for difference in group means that does not contain 0, click Compare Means under Oneway Analysis (after Analyze, Fit Y by X) and click All Pairs, Tukey’s HSD. In table “Comparison of All Pairs Using Tukey’s HSD,” two groups are significantly different if and only if the entry in the table for the pair of groups is positive.

13 Bonferroni Method Bonferroni Method: If we have a family of k confidence intervals, to form individual confidence intervals that have a familywise confidence level of 95%, make the individual confidence intervals have confidence level 100(1-.05/k)%. General method for doing multiple comparisons. Bonferroni Inequality:

14 Bonferroni for tests Suppose we are conducting k hypothesis tests and will “reject” the null hypothesis if the p-value is smaller than a cutoff p* (e.g., p* =.05). Per-test type I error rate: the probability of falsely rejecting the null hypothesis when it is true. The per-test type I error rate is p*. Familywise error rate: the probability of falsely rejecting at least one null hypothesis in a family of tests when all null hypotheses are true. Bonferroni for tests: For a family of k tests, use a cutoff of p*/k to obtain a familywise error rate of at most p*, e.g., for ten tests, reject if p-value <0.005 to obtain familywise error rate of at most 0.05.

15 Bonferroni for mice diets Five comparisons were planned. Suppose we want the familywise error rate for the five comparisons to be 0.05. Bonferroni method: We should consider two groups to be significantly different if the p-value from the two-sided t-test is less than 0.05/5=0.01.

16 Exploratory Data Analysis and Multiple Comparisons Searching data for suggestive patterns can lead to important discoveries but it is difficult to test a hypothesis against a data set which suggested it. We must protect against “data snooping.” One way to try to protect against data snooping is to use multiple comparisons procedures (e.g., example in Section 6.5.2). The best way to protect against data snooping is to design a study to search specifically for a pattern that was suggested by an exploratory data analysis. In other words we convert an “unplanned” comparison into a “planned” comparison by doing a new experiment.

17 Review of One-way layout Assumptions of ideal model –All populations have same standard deviation. –Each population is normal –Observations are independent Planned comparisons: Usual t-test but use all groups to estimate. If many planned comparisons, use Bonferroni to adjust for multiple comparisons Test of vs. alternative that at least two means differ: one-way ANOVA F-test Unplanned comparisons: Use Tukey-Kramer procedure to adjust for multiple comparisons.

18 Review example Case Study 6.1.1: Discrimination against the handicapped. Randomized experiment to study how physical handicaps affect people’s perception of employment qualifications. Researchers prepared five videotaped job interviews with same two male actors in each. Tapes differed only in that applicant appeared with a different handicap: wheelchair, crutches, hearing impaired, leg amputated, no handicap. Seventy undergraduates were randomly assigned to view the tapes, 14 to each.

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