Stat individual Assignment (1).docx

Some have argued that a one tailed test is justified

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Some have argued that a one tailed test is justified whenever the researcher predicts the direction of an effect. The problem with this argument is that if the effect comes out strongly in the non- predicted direction, the researcher is not justified in concluding that the effect is not zero. Since this is unrealistic, one tailed tests are usually viewed skeptically if justified on this basis alone. SIMPLE HYPOTHESIS AND COMPOSITE HYPOTHESIS SIMPLE HYPOTHESIS
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In order to explain simply, a simple hypothesis is one where all parameters of the distribution are specified. For example, if the heights of the school are generally distributed with s 2 =4, the hypothesis that its mean μ , is say 62, that is Ho=62. This is a simple hypothesis as the mean and variance both of them together specify a normal distribution. In general a simple hypothesis indicates that θ = θ 0 , where θ 0 is the specified value of the parameter θ , where θ may indicate μ, ρ, μ 1 μ 2 etc. you can answer all your queries through simple and composite hypothesis assignment. COMPOSITE HYPOTHESIS Alternatively, a hypothesis which is not simple is called a composite hypothesis. For example, if we consider that H0 : μ > 62 and s 2 =4 or H0: μ =62 and s 2 =4, then this hypothesis becomes a composite hypothesis. In this case, the exact distribution of the population is not known in both the cases. The common form of composite hypothesis is θ < θ 0 or θ > θ 0 . It means that the parameter θ does not exceed beyond or does not fall short of the specified value θ 0 . This concept of simple and composite hypothesis is applicable to both alternative hypothesis and null hypothesis. These statistical hypothesis may sound complicated at the beginning with our prolonged effort have been successful in lending support with simple and composite hypothesis homework help.
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Want to know that the differences between simple and composite hypothesis? A simple hypothesis specifies the distribution completely: where as a composite hypothesis does not. When you want to test a simple null hypothesis with a composite alternative, the power of the test will be the function of the parameter of interest. Along with it, the power also varies by the sample size and thus gets affected. Want to learn this hypothesis in an easier way, and then just go through simple and composite hypothesis assignment help to get the perfect approach to learn. Why do you think so many people around the globe trust us. ACCETANCE AND REJECTION REGIONS.
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All possible values which a test-statistic may assume can be divided into two mutually exclusive groups: one group consisting of values which appear to be consistent with the null hypothesis and the other having values which are unlikely to occur if H0 is true. The first group is called the acceptance region and the second set of values is known as the rejection region for a test. The rejection region is also called the critical region. The value that separates the critical region from the acceptance region is called the critical values. The critical value which can be in the same units as the parameter or in the standardized units, is to be decided by the experimenter keeping in view the degree of confidence they are willing to have in the null hypothesis.
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  • Spring '17
  • KASMIS MISGANEW
  • Null hypothesis, Statistical hypothesis testing, researcher, Type I and type II errors

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