**Statistical Reporting
and Interpretation**

**Answers**

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1.
a

2. b

3. c

4. c

5. e

6. The p-value
summarizes results of tests of the same null vs. the same alternative
hypothesis at all possible values of a. Tests using values of a no
smaller than the p-value lead to rejection of the null hypothesis, while tests
at any other values of a lead to retention of the null hypothesis. The
p-value is a useful way to report hypothesis tests because readers who require
different standards of evidence in order to reject the null hypothesis may use
the same p-value to see whether the evidence meets their particular standards.
The p-value can also be used as a measure of the compatibility of the data with
the null hypothesis, with a p-value close to 1 indicating strong compatibility
with the null hypothesis, and a p-value close to 0 indicating strong
incompatibility.

The confidence
interval, on the other hand, summarizes the results of tests of all
possible assumed "null" values of a given parameter, including an
actual null value of 0 and all specific non-null values (e.g. 1, 2, -5, 10)
representing effects of particular direction and strength, at the same value of
a = 1-(confidence coefficient of the interval).