Null hypothesis testing

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  • When you have completed this chapter, you will be able to: Define null and alternative hypothesis and hypothesis testing, define Type I and Type II errors, describe the five-step hypothesis testing procedure, distinguish between a one-tailed and a two-tailed test of hypothesis,...

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  • CHƯƠNG 3: KIỂM ÐỊNH GIẢ THUYẾT (Hypothesis Testing) I. II. III. IV. V. VI. VII. VIII. IX. KHÁI NIỆM QUY TRÌNH TỔNG QUÁT TRONG KIỂM ĐỊNH GIẢ THUYẾT CÁC LOẠI GIẢ THUYẾT TRONG THỐNG KÊ 1. Giả thuyết H0 : (The null hypothesis) 2. Giả thuyết H1 : (The Alternative Hypothesis) CÁC LOẠI SAI LẦM TRONG KIỂM ĐỊNH GIẢ THUYẾT 1. Sai lầm loại I 2. Sai lầm loại II KIỂM ĐỊNH TRUNG BÌNH TỔNG THỂ 1.

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  • In the Essentials of Behavioral Science series, our goal is to provide readers with books that will deliver key practical information in an efficient, accessible style. The series features books on a variety of topics, such as statistics, psychological testing, and research design and methodology, to name just a few. For the experienced professional, books in the series offer a concise yet thorough review of a specific area of expertise, including numerous tips for best practices.

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  • The #1 selling medical review book in the world. This book manages to fit two years worth of medical school content into one review book....This book is extremely helpful in preparing for the USMLE Step 1 exam. It is concise enough to use to prepare for the exam in a reasonable amount of time without omitting important information. It covers the material covered in the first two years of medical school very well. Service First Aid for the USMLE Step 1 is the undisputed "bible" of USMLE Step 1 preparation. It provides a complete framework to help you prepare for...

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  • Null hypothesis significance testing (NHST) is one of the main research tools in social and behavioral research. It requires the specification of a null hypothesis, an alternative hypothesis, and data in order to test the null hypothesis. The main result of a NHST is a p-value [3]. An example of a null hypothesis and a corresponding alternative hypothesis for a one-way analysis of variance is:

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  • As with every hypothesis test, inference based on alpha estimates can lead to the detection of a lucky fund, namely a fund with a significant estimated alpha, while its true alpha is equal to zero. The difficulty raised by the standard approach is that it implies a multiple hypothesis test since the null hypothesis of no performance is not tested once, but M times. Accounting for the presence of luck in a multiple testing framework is much more complex, because luck cannot be measured by the significance level applied to each fund.

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  • In the usual case where a single test is performed on the alpha of one fund (or one portfolio of funds), luck is controlled by setting the significance level γ (or equivalently the Size of the test). The standard approach differs from this framework because it boils down to running a multiple hypothesis test instead of a single one. The null hypothesis H0 of no performance is tested for each of the M funds in the population. In a multiple testing framework, luck refers to the number (or the proportion) of lucky funds among the significant funds that are discovered.

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  • We used t-tests with robust standard errors (to account for clustering caused by multiple nutrient comparisons within studies) to test the null hypothesis of no evidence of a difference between organically and conventionally produced food in content of nutrients and other substances. P-values were calculated to determine the significance of observed differences; p-values of less than 0.05 were used as a basis for evidence of significant differences between organically and conventionally produced foodstuffs.

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  • The reliability of the DPD results depends crucially on the assumption that the instruments are valid. This can be checked by employing the Hansen test of overidentifying restrictions. A rejection of the null hypothesis that instruments are uncorrelated to errors would indicate inconsistent estimates. In addition, we also present test statistics for second-order serial correlation in the error process.

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  • A number of recent papers document a link between mood and stock returns. Convincing arguments that such results are not simply the product of data mining call for investigating a new mood variable or testing an existing mood variable on an independent sample to confirm results of previous studies. For example, Hirshleifer and Shumway (2003) confirm and extend the sunlight effect first documented by Saunders (1993).

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