PPT-Hypothesis Testing: Significance

STAT 250 Dr Kari Lock Morgan SECTION 43 Significance level 43 Statistical conclusions 43 pvalue and H 0 If the pvalue is small then a statistic as extreme as that observed would be unlikely if the null hypothesis were true providing significant evidence against H. Hypothesis. testing. The process of making judgments about a large group (population) on the basis of a small subset of that group (sample) is known as statistical inference.. Hypothesis testing, one of two fields in statistical inference, allows us to objectively assess the probability that statements about a population are true. . . Testing. Martina Litschmannová. m. artina.litschmannova. @vsb.cz. K210. Terms Introduce in Prior Chapter. Population. . …. . all possible values. Sample. . …. a portion of the population. Formalizing. We saw in the last section how to find a confidence interval. In this section, we . use the . confidence interval to come up with a formal test to be able to say whether or not we think a sample is representative of the population. We assume n is at least 30, and hence may use s to estimate . +. Probability. Seminar 6. A difficult mock question for mid-term. Plot . the following graph. People . who use Facebook only (but not Twitter) are generally happier than those who use Twitter only (but not Facebook). This . University of La Verne. Soomi Lee, . Ph.D. Week 5. What you will learn in Chapter 9. Population vs. Sample. Parameters vs. Statistics. Sampling error. Probability. P. roperties of the Normal . C. urve. 1. Contents:. Tests of significance for small samples. Student’s t- test. Properties of t-Distribution. The t- table. Application of the t-Distribution. To test the significance of . Mean of a random sample. b. elieve it (or not). Our belief decision must ultimately stand on three legs:. What does our general background knowledge and experience tell us (for example, what is the reputation of the speaker)?. Test of hypothesis - Test whether a population parameter is less than, equal to, or greater than a specified value.. Remember an inference without a measure of reliability is little more than a guess.. Chong Ho Yu (Alex). Ford's Model T in Statistics. Most statistical procedures that we still use today were invented in the late 19. th. century or early 20. th. century.. The t-test was introduced by William Gosset in 1908.. Sample. , shape, location, and spread. Sample = make sure it's random, handle missing data (mcar, mar, nmar), imputation . methods. NMAR!. . Shape = Is the data skewed, normal, or flat? If normal then we can use statistical analysis for normal . Type I & II Error and Test of 1 &2 Tailed Hypothesis Khagendra Kumar Dept. of Education Patna University Decision Making on Accepting & Rejecting Hypotheses  To take decision for accepting or I. Terms, Concepts. A. In general, we do not know the true value of population parameters - they must be estimated. However, we do have hypotheses about what the true values are. B. The major Inferential Statistics: Making inferences about populations based on samples. Chapter Outline. Hypothesis-Testing. One-Tailed and Two-Tailed Hypothesis Tests. Decision Errors. Decision Errors. When the right procedures lead to the wrong decisions. In . statistics. , a . Type I error.  is a false positive conclusion, while a . Type II error.  is a false negative conclusion.. The probability of making a Type I error is the significance level, or alpha (α), while the probability of making a Type II error is beta (β). These risks can be minimized through careful planning in your study design..