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,...
CHƯƠNG 3: KIỂM ÐỊNH GIẢ THUYẾT (Hypothesis Testing)
I. II. III.
VI. VII. VIII.
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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Chapter 9 - Hypothesis testing. After mastering the material in this chapter, you will be able to: Set Up appropriate null and alternative hypotheses, describe Type I and Type II errors and their probabilities, use critical values and p-values to perform a z test about a population mean when s is known,...
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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 . An example of a null hypothesis and
a corresponding alternative hypothesis for a one-way analysis of variance is:
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.
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.
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.
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.
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).