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The Statistics of Hypothesis
The Statistics of Hypothesis

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Independent t-Test

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BBA2: STT 200

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... In this question the p- value is calculated as twice the P(z * ≥ 2.174) , which from the Normal tables is estimated as 2(1-0.9850) = 0.03. Recall that we specified a two-sided test at the onset and hence we need to double the probability as the rejection region is split across both tails of the dist ...
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... Reject the null hypothesis. The data provide sufficient evidence that the average distance is greater than 200 yards. Accept the null hypothesis. The data provide sufficient evidence that the average distance is greater than 200 yards. Reject the null hypothesis. The data do not provide sufficient e ...
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... size. Following the recommendation by Agresti and Coull (1998), Brown et al. (2001) and others, the Wald interval should not be used in practice. In actual applications, only finite samples are available and the location of parameter is unknown. From the frequentist point of view, it is reasonable t ...
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... Reject the null hypothesis. The data provide sufficient evidence that the average distance is greater than 200 yards. Accept the null hypothesis. The data provide sufficient evidence that the average distance is greater than 200 yards. Reject the null hypothesis. The data do not provide sufficient e ...
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... of evidence against the null hypothesis. The farther away the observations from what we would expect if the H0 were true, the more evidence there is against H0 . The z-statistic we calculated earlier is one way of measuring how far the data are from what we would expect, which is what allows us to d ...
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Slide 1
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5. 7 Theory of Subjective Probability
5. 7 Theory of Subjective Probability

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Foundations of statistics

Foundations of statistics is the usual name for the epistemological debate in statistics over how one should conduct inductive inference from data. Among the issues considered in statistical inference are the question of Bayesian inference versus frequentist inference, the distinction between Fisher's ""significance testing"" and Neyman-Pearson ""hypothesis testing"", and whether the likelihood principle should be followed. Some of these issues have been debated for up to 200 years without resolution.Bandyopadhyay & Forster describe four statistical paradigms: ""(1) classical statistics or error statistics, (ii) Bayesian statistics, (iii) likelihood-based statistics, and (iv) the Akaikean-Information Criterion-based statistics"".Savage's text Foundations of Statistics has been cited over 10000 times on Google Scholar. It tells the following.It is unanimously agreed that statistics depends somehow on probability. But, as to what probability is and how it is connected with statistics, there has seldom been such complete disagreement and breakdown of communication since the Tower of Babel. Doubtless, much of the disagreement is merely terminological and would disappear under sufficiently sharp analysis.
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