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Two-tailed directional. A two-tailed directional alternative hypothesis is concerned with both regions of rejection of the sampling distribution. Non-directional. A non-directional alternative hypothesis is not concerned with either region of rejection; rather, it is only concerned that null hypothesis is not true.
"We may, however, choose any null hypothesis we please, provided it is exact." Regarding an alternative non-directional significance test of the Lady tasting tea experiment: "For this purpose the new test proposed would be entirely inappropriate, and no experimenter would be tempted to employ it.
A two-tailed test is appropriate if the estimated value is greater or less than a certain range of values, for example, whether a test taker may score above or below a specific range of scores. This method is used for null hypothesis testing and if the estimated value exists in the critical areas, the alternative hypothesis is accepted over the ...
A sample of 10 consumers are each given product A and product B, and asked which product they prefer. The null hypothesis is that consumers do not prefer product B over product A. The alternative hypothesis is that consumers prefer product B over product A. This is a one-sided (directional) test.
An example of Neyman–Pearson hypothesis testing (or null hypothesis statistical significance testing) can be made by a change to the radioactive suitcase example. If the "suitcase" is actually a shielded container for the transportation of radioactive material, then a test might be used to select among three hypotheses: no radioactive source ...
where ¯ is the sample mean and ^ is the unbiased sample variance. Since the right hand side of the second equality exactly matches the characterization of a noncentral t -distribution as described above, T has a noncentral t -distribution with n −1 degrees of freedom and noncentrality parameter n θ / σ {\displaystyle {\sqrt {n}}\theta ...
A statistical significance test starts with a random sample from a population. If the sample data are consistent with the null hypothesis, then you do not reject the null hypothesis; if the sample data are inconsistent with the null hypothesis, then you reject the null hypothesis and conclude that the alternative hypothesis is true. [3]
The consistent application by statisticians of Neyman and Pearson's convention of representing "the hypothesis to be tested" (or "the hypothesis to be nullified") with the expression H 0 has led to circumstances where many understand the term "the null hypothesis" as meaning "the nil hypothesis" – a statement that the results in question have ...