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. . 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 . Note:. In Chapter 8 we used methods of estimating the value of a parameter.. In this chapter, we are drawing inferences about the parameter by making decisions concerning the value of the parameter. Two Hypothesis:. +. 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 . Mr. Mark Anthony Garcia, M.S.. Mathematics Department. De La Salle University. Situation: Hypothesis Testing. Suppose that a political analyst predicted that senatorial candidate A will top the upcoming senatorial elections in city X with at least 0.70 or 70% of the votes.. Preliminaries. We wish to test whether a particular assumption/claim regarding the population is true or not.. Null Hypothesis (H0) – original assumption. Alternative Hypothesis (H1) . Determine a critical value to determine whether or not to reject Ho. 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)?. Hypothesis Testing. A statement has been made. We must decide whether to . 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)?. Copyright © Cengage Learning. All rights reserved. 8 Tests of Hypotheses Based on a Single Sample Copyright © Cengage Learning. All rights reserved. 8.5 Further Aspects of Hypothesis Testing Statistical Versus Practical Significance A nuclear power plant adjacent to a residential area. STATISTICS FOR BUSINESS. (Hypothesis testing for a single population). A nuclear power plant. adjacent to a residential area. Two . local . residents die . 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 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.. m. artina.litschmannova. @vsb.cz. EA 538. Terms Introduce in Prior Chapter. Population. . …. . all possible values. Sample. . …. a portion of the population. . Statistical inference . …. .