1STAT 13, UCLA, Ivo DinovSlide 1UCLA STAT 13Introduction toStatistical Methods for the Life and Health Sciences!Instructor: Ivo Dinov, Asst. Prof. In Statistics and Neurology!Teaching Assistants:Ming Zheng, Sovia Lau, Tianwei YuUCLA StatisticsUniversity of California, Los Angeles, Fall 2003http://www.stat.ucla.edu/~dinov/courses_students.htmlSTAT 13, UCLA, Ivo DinovSlide 2Chapter 9: Significance Testing --Using Data to Test Hypotheses!Getting Started!What do we test? Types of hypotheses!Measuring the evidence against the null!Hypothesis testing as decision making!Why tests should be supplemented by intervalsSTAT 13, UCLA, Ivo DinovSlide 3ESP (extra sensory perception) or just guessing?0.198 0.200 0.202 0.204 0.206 0.208True value forjust guessing (0.200)Pratt & Woodruff’sproportion (0.2082)Deck of equalnumber of Zener/Rhinecardsn=60,000 random drawsresulting in 12,489 correct guessesCan sampling variations alone account for Pratt & Woodruff’s success rate = 20.82% correct vs. 20% expected.Sample proportionsFrom 7 just-guessing gamesSTAT 13, UCLA, Ivo DinovSlide 4ESP or just guessing?0.196 0.198 0.200 0.202 0.204 0.206 0.2080.194True value for just guessingPratt & Woodruff’sproportionFigure 9.1.1Sample proportions from 400 “just-guessing” experiments.From Chance Encounters by C.J. Wild and G.A.F. Seber, © John Wiley & Sons, 2000.Computer simulationmaking 60,000 guesseswith 20% chance ofcorrect guess.STAT 13, UCLA, Ivo DinovSlide 5Was Cavendish’s experiment biased?A number of famous early experiments of measuring physical constants have later been shown to be biased.Mean density of the earthTrue value = 5.517Cavendish’s data: (from previous Example 7.2.2)5.36, 5.29, 5.58, 5.65, 5.57, 5.53, 5.62, 5.29, 5.44, 5.34, 5.79, 5.10,5.27, 5.39, 5.42, 5.47, 5.63, 5.34, 5.46, 5.30, 5.75, 5.68, 5.85n = 23, sample mean = 5.483, sample SD = 0.1904STAT 13, UCLA, Ivo DinovSlide 6Was Cavendish’s experiment biased?5.45 5.50 5.55 5.60Truevalue (5.517)Cavendishmean (5.483)21.5% of the means weresmaller than this.0335 .0335Figure 9.1.2Sample means from 400 sets of observationsfrom an unbiased experiment.SD=0.1904SD=0.1904N(5.517,0.1904)Simulate taking400 sets of 23measurementsfromN(5.517,0.1904).Plotted are theresults of thesample means.Are the Cavendishvalues unusuallydiff. from truemean?2STAT 13, UCLA, Ivo DinovSlide 7Cavendish: measuring distances in std errors-3 -2 -1012320.5% of samples had tvalues smaller than this.844 .844Cavendish t -value = 0.84400Figure 9.1.3 Sample t -values from 400 unbiased experiments (each t -value is distance between sample mean and 5.517 in std errors).00Cavendishdata lies withinthe central 60% of the distributionSTAT 13, UCLA, Ivo DinovSlide 8-3 -2 -1012320.5% of samples had tvalues smaller than this.844 .844Cavendish t -value = 0.84400Figure 9.1.3 Sample t -values from 400 unbiased experiments (each t -value is distance between sample mean and 5.517 in std errors).From Chance Encounters by C.J. Wild and G.A.F. Seber, © John Wiley & Sons, 2000.00-3 -2 -1 0 1230.204 0.2040.844 0.844Figure 9.1.4Student(df=22) density.From Chance Encounters by C.J. Wild and G.A.F. Seber, © John Wiley & Sons, 2000.STAT 13, UCLA, Ivo DinovSlide 9Measuring the distance between the true-value and the estimate in terms of the SE! Intuitive criterion: Estimate is credible if it’s not far away from its hypothesized true-value!! But how far is far-away?! Compute the distance in standard-terms:! Reason is that the distribution of T is known in some cases (Student’s t, or N(0,1)). The estimator (obs-value) is typical/atypical if it is close to the center/tail of the distribution.SEterValueTrueParameEstimatorT−=STAT 13, UCLA, Ivo DinovSlide 10Comparing CI’s and significance tests! These are different methods for coping with the uncertainty about the true value of a parameter caused by the sampling variation in estimates.! Confidence interval: A fixed level of confidence is chosen. We determine a range of possible values for the parameter that are consistent with the data (at the chosen confidence level).! Significance test: Only one possible value for the parameter, called the hypothesized value, is tested. We determine the strength of the evidence (confidence) provided by the data against the proposition that the hypothesized value is the true value.STAT 13, UCLA, Ivo DinovSlide 11Review! What intuitive criterion did we use to determine whether the hypothesized parameter value (p=0.2 in the ESP Example 9.1.1, and µ= 5.517 in Example 9.1.2) was credible in the light of the data? (Determine if the data-driven parameter estimate is consistent with the pattern of variation we’d expect get if hypothesis was true. If hypothesized value is correct, our estimate should not be far from its hypothesized true value.)! Why was it that µ= 5.517 was credible in Example 9.1.2, whereas p=0.2 was not credible in Example 9.1.1? (The first estimate is consistent, and the second one is not, with the pattern of variation of the hypothesized true process.)STAT 13, UCLA, Ivo DinovSlide 12Review! What do t0-values tell us? (Our estimate is typical/atypical, consistent or inconsistent with our hypothesis.)! What is the essential difference between the information provided by a confidence interval (CI) and by a significance test (ST)? (Both are uncertainty quantifiers. CI’s use a fixed level of confidence to determine possible range of values. ST’s one possible value is fixed and level of confidence is determined.)3STAT 13, UCLA, Ivo DinovSlide 13Guiding principlesWe cannotrule in a hypothesized value for a parameter, we can only determine whether there is evidence to rule out a hypothesized value.The null hypothesis tested is typically a skeptical reactionto a research hypothesisHypothesesSTAT 13, UCLA, Ivo DinovSlide 14Comments! Why can't we (rule-in) prove that a hypothesized value of a parameter is exactly true?(Because when constructing estimates based on data, there’s always sampling and may be non-sampling errors, which are normal, and will effect the resulting estimate. Even if we do 60,000 ESP tests, as we saw earlier, repeatedly we are likely to get estimates like 0.2 and 0.200001, and 0.199999, etc. – non of which may be exactly the theoretically correct, 0.2.)! Why use the rule-out principle? (Since, we can’t use the rule-in method, we try to find compelling evidence against the observed/data-constructed estimate – to reject it.)! Why
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