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Australian Critical Care, 2009
Publisher Summary This chapter introduces the statistical hypothesis testing, which is concerned with using data to test the plausibility of a specified hypothesis. Such test might reject the hypothesis that fewer than 44 percent of Midwestern lakes are afflicted by acid rain.
Sandra M C, Pereira, Gavin, Leslie
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Publisher Summary This chapter introduces the statistical hypothesis testing, which is concerned with using data to test the plausibility of a specified hypothesis. Such test might reject the hypothesis that fewer than 44 percent of Midwestern lakes are afflicted by acid rain.
Sandra M C, Pereira, Gavin, Leslie
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2003
Abstract The previous chapter described confidence intervals, which provide a fundamental strategy for making inferences about population measures of location such as the population mean μ and the population median. About a century after Laplace’s ground breaking work on sampling distributions, a new set of tools was developed for making
Wolfgang Karl Härdle, Léopold Simar
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Abstract The previous chapter described confidence intervals, which provide a fundamental strategy for making inferences about population measures of location such as the population mean μ and the population median. About a century after Laplace’s ground breaking work on sampling distributions, a new set of tools was developed for making
Wolfgang Karl Härdle, Léopold Simar
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2018
In this section, we shall discuss another way to deal with the problem of making a statement about an unknown parameter associated with a probability distribution, based on a random sample. Instead of finding an estimate for the parameter, we shall often find it convenient to hypothesize a value for it and then use the information from the sample to ...
Dharmaraja Selvamuthu, Dipayan Das
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In this section, we shall discuss another way to deal with the problem of making a statement about an unknown parameter associated with a probability distribution, based on a random sample. Instead of finding an estimate for the parameter, we shall often find it convenient to hypothesize a value for it and then use the information from the sample to ...
Dharmaraja Selvamuthu, Dipayan Das
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2019
Hypothesis testing is a statistical decisional process that allows one to choose between two complementary possibilities on the basis of samples drawn from the population(s) of interest. The two possibilities are called the null and alternative hypothesis, respectively.
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Hypothesis testing is a statistical decisional process that allows one to choose between two complementary possibilities on the basis of samples drawn from the population(s) of interest. The two possibilities are called the null and alternative hypothesis, respectively.
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Clinical Nurse Specialist, 1996
Hypothesis testing is the process of making a choice between two conflicting hypotheses. The null hypothesis, H0, is a statistical proposition stating that there is no significant difference between a hypothesized value of a population parameter and its value estimated from a sample drawn from that population. The alternative hypothesis, H1 or Ha, is a
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Hypothesis testing is the process of making a choice between two conflicting hypotheses. The null hypothesis, H0, is a statistical proposition stating that there is no significant difference between a hypothesized value of a population parameter and its value estimated from a sample drawn from that population. The alternative hypothesis, H1 or Ha, is a
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2015
Statistics plays an important role in decision making. In statistics, one utilizes random samples to make inferences about the population from which the samples were obtained. Statistical inference regarding population parameters takes two forms: estimation and hypothesis testing, although both may be viewed as different aspects of the same general ...
Kandethody M. Ramachandran +1 more
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Statistics plays an important role in decision making. In statistics, one utilizes random samples to make inferences about the population from which the samples were obtained. Statistical inference regarding population parameters takes two forms: estimation and hypothesis testing, although both may be viewed as different aspects of the same general ...
Kandethody M. Ramachandran +1 more
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2012
Several key statistical concepts are fundamental not only for hypothesis tests but also for most statistical analyses that arise in clinical studies. Commonly used terms, such as critical values, p-values, and type I and type II errors are defined.
Craig B. Borkowf +2 more
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Several key statistical concepts are fundamental not only for hypothesis tests but also for most statistical analyses that arise in clinical studies. Commonly used terms, such as critical values, p-values, and type I and type II errors are defined.
Craig B. Borkowf +2 more
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, 1980 The most general situation so far has been testing θ ≤ θ0 against θ> θ0. We next wish to consider testing θ1≤θ≤θ2 against the two-sided alternative θ θ2. We can scarcely hope for a uniformly most powerful test for it would have to compete with the best available tests against the one-sided alternatives θ θ2 taken separately.
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On Testing the Utility Hypothesis
The Economic Journal, 1997In order to be able to conduct a test of the (core) utility hypothesis that is not confounded with tests of (subsidiary) hypotheses that economic agents all have the same preferences and that their preferences are weakly separable, it is necessary to use data that are disaggregated and complete.
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