assesment week 2
A p-value is the probability of getting a test statistic less extreme than the one observed. If the null hypothesis is false.
False
A p-value is the probability of rejecting the null hypothesis if it is, in fact, false.
False
A p-value is the probability the null hypothesis is true given a particular observed statistic.
False
A well-planned test of significance should result in a statement either that the null hypothesis is true or that it is false.
False
Failure to reject H0 means that the null hypothesis is probably true.
False
If the p-value for a test is 0.043, the probability that the null hypothesis is true is 0.043.
False
If the p-value is 0.05, the probability that the null hypothesis is correct is 0.05.
False
It is helpful to examine your data before deciding whether to use a one-sided or a two-sided hypothesis test.
False
Rejection of H0 confirms the alternative hypothesis.
False
The larger the p-value, the more evidence there is against the null hypothesis.
False
The null hypothesis is one-sided and expressed using either < or > if there is interest in deviations in only one direction.
False
The null hypothesis states that a sample statistic is equal to some value, whereas the alternative hypothesis is either greater, less-than, or not equal to some value.
False
The p-value can be negative or positive depending upon whether the sample statistic is less than or greater than the claimed value of the population parameter in the null hypothesis.
False
The p-value is based on a specific test statistic so must be chosen before an experiment is conducted.
False
A p-value is a conditional probability.
True
If a population parameter is known, there is no reason to run a hypothesis test on that population parameter.
True
In testing the null hypothesis H0 : u = 75 against the alternative hypothesis Ha : u > 75, a sample from a normal population has a mean of 76.9 and a corresponding p-value of 0.0175. There is sufficient evidence to reject the null hypothesis.
True
Test of significance (hypothesis tests) are designed to measure the strength of evidence against the null hypothesis given a sample.
True
The null hypothesis states that a population parameter is equal to some value, whereas the alternative hypothesis is either greater, less-than, or not equal to that same value.
True
The p-value of a test is the probability of obtaining a result as extreme as the one obtained assuming that the null hypothesis is true.
True