Question

Determine whether the BEST, most common interpretation of the given statement is:

     TRUE - Select 1

     FALSE - Select 2

Question 1 options:

1	Type II error is the probability of failing to reject H0 when it is false.
2	In hypothesis testing, the ALTERNATIVE hypothesis states how the parameter of interest differs from the null hypothesis value.
3	The p-value is determined by the ALTERNATIVE hypothesis.
4	The p-value is the probability the null hypothesis is true.
5	In hypothesis testing, the test of hypothesis is developed assuming the ALTERNATIVE hypothesis is true.
6	For larger sample sizes (n>30) with the STANDARD DEVIATION UNKNOWN, the t-distribution should be used to test hypotheses about the population mean.
7	The LARGER the significance level, the stronger the evidence needed to reject H0.
8	Type I error can be defined as the probability the null hypothesis is true.
9	In hypothesis testing, the NULL hypothesis states that a parameter of interest is equal to a specific value.
10	The SIGNIFICANCE LEVEL determines how unusual an outcome must be in order to reject or fail to reject the null hypothesis.

Answer 1

Ans:

1)True

2)False(Alternative hypothesis is to compare a parameter with hypothized value)

3)True

The P value, or calculated probability, is the probability of finding the observed, or more extreme, results when the null hypothesis (H ₀) of a study question is true , the definition of 'extreme' depends on how the hypothesis is being tested.

4)False,see definition p-value above

5)False,assuming null hypothesis is true

6)False,For n>30,we can use z distribution

7)False,lower the significance level,the stronger the evidence needed

8)False,type I error is when we reject null hypothesis,but null hypothesis is true.

9)True

10)True