Question

As we get into hypothesis testing I often think about the treatment of a patient. How can you relate hypothesis testing to the medical field? Think of an example of how hypothesis testing could be used and comment on what your classmates come up with.

Answer 1

ANSWER::

Hypothesis: (Problem is to test)

1. There are 2 statistical hypotheses involved in hypothesis testing. These should be stated a prior and explicitly. The null hypothesis is the hypothesis to be tested. It is denoted by the symbol H0.
2. It is also known as the hypothesis of no difference. The null hypothesis is set up with the sole purpose of efforts to knock it down. In the testing of hypothesis, the null hypothesis is either rejected (knocked down) or not rejected (upheld).
3. If the null hypothesis is not rejected, the interpretation is that the data is not sufficient evidence to cause rejection. If the testing process rejects the null hypothesis, the inference is that the data available to us is not compatible with the null hypothesis and by default we accept the alternative hypothesis, which in most cases is the research hypothesis. The alternative hypothesis is designated with the symbol HA or H1.

Test Statistic:

1. The test statistic is the statistic that is derived from the data from the sample. Evidently, many possible values of the test statistic can be computed depending on the particular sample selected.
2. The test statistic serves as a decision maker, nothing more, nothing less, rather -than proof or lack of it. The decision to reject or not to reject the null hypothesis depends on the magnitude of the test statistic.

Statistical Design analysis:

1. The statistical decision consists of rejecting or of not rejecting the null hypothesis. It is rejected if the computed value of the test statistic falls in the rejection region, and it is not rejected if the value falls in the non-rejection region.

Conclusion:

• If H0 is rejected, we conclude that HA is true. If H0 is not rejected, we conclude that H0 may be true.

P value:

• A p value indicating that the sample results are not likely to have occurred. If the null hypothesis is true provides reason for doubting the truth of the null hypothesis

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